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https://github.com/innovation-cat/Awesome-Federated-Machine-Learning

Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
https://github.com/innovation-cat/Awesome-Federated-Machine-Learning

List: Awesome-Federated-Machine-Learning

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Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

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README 

        
# Awesome Federated Machine Learning [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) 

Federated Learning (FL) is a new machine learning framework, which enables multiple devices collaboratively to train a shared model without compromising data privacy and security.  





FL




This repository aims to keep tracking the latest research advancements of federated learning, including but not limited to research papers, books, codes, tutorials, and videos.



## Table of Contents



 - [Top Machine Learning Conferences](#top-machine-learning-conferences)

     + [ICML](#icml) [ICLR](#iclr) [NeurIPS](#neurips)   

 - [Top Computer Vision Conferences](#top-computer-vision-conferences)

     + [CVPR](#cvpr) [ICCV](#iccv) [ECCV](#eccv)   

 - [Top Artificial Intelligence and Data Mining Conferences](#top-artificial-intelligence-and-data-mining-conferences)

     + [AAAI](#aaai) [AISTATS](#aistats) [KDD](#kdd) 	 

 - [Books](#books)

 - [Papers (Research directions)](#papers)

     + [Model Aggregation](#1-model-aggregation)

	 + [Personalization](#2-personalization)   

	 + [Recommender system](#3-recommender-system)

	 + [Security](#4-security)  

	 + [Survey](#5-survey)  

	 + [Efficiency](#7-efficiency)  

	 + [Optimization](#8-optimization)   

	 + [Fairness](#9-fairness)


	 + [Application](#10-applications)  

	 + [Boosting](#11-boosting)  

	 + [Incentive mechanism](#12-incentive-mechanism)

	 + [Unsupervised Learning](#13-unsupervised-learning)  

	 + [Heterogeneity](#14-heterogeneity)  

	 + [Client Selection](#15-client-selection)

	 + [Graph Neural Networks](#16-graph-neural-networks)

	 + [Other Machine Learning Paradigm](#18-other-machine-learning-paradigm)

	 + [Computational Learning Theory](#19-computational-learning-theory)

 - [Google FL Workshops](#google-fl-workshops)	 

 - [Videos and Lectures](#videos-and-lectures)

 - [Tutorials and Blogs](#tutorials-and-blogs)

 - [Open-Sources](#open-sources)

     + [Enterprise Grade](#enterprise-grade)

	 + [Research Purpose](#research-purpose)



  



## Top Machine Learning Conferences



In this section, we will summarize Federated Learning papers accepted by top machine learning conference, Including NeurIPS, ICML, ICLR.



### ICML 



    

	

	

	

	

		Years

		Title

		Affiliations

		Materials

	

	

        ICML 2023

        A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates

        Accenture Labs

		

	

    

		Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning

        ESAT-PSI, KU Leuven

		code

	

	

		LeadFL: Client Self-Defense against Model Poisoning in Federated Learning

        Delft University of Technology

		

	

	

		Achieving Linear Speedup in Non-IID Federated Bilevel Learning

        Meta

		

	

	

		On the Convergence of Federated Averaging with Cyclic Client Participation

        Carnegie Mellon University

		

	

	

		FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models

        The Hong Kong University of Science and Technology

		

	

	

		Flash: Concept Drift Adaptation in Federated Learning

        University of Massachusetts

		code

	

	

		Personalized Federated Learning under Mixture of Distributions

        University of California, Los Angeles

		

	

	

		Federated Heavy Hitter Recovery under Linear Sketching

		Google

		

	

	

		Improving the Model Consistency of Decentralized Federated Learning

        Tsinghua University

		

	

	

		Towards Unbiased Training in Federated Open-world Semi-supervised Learning

        The Hong Kong Polytechnic University

		

	

	

		Optimizing the Collaboration Structure in Cross-Silo Federated Learning

        University of Illinois Urbana-Champaign

		code

	

	

		FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction

        The Chinese University of Hong Kong

		code

	

	

		Federated Online and Bandit Convex Optimization

        Toyota Technology Institute

		

	

	

		Federated Linear Contextual Bandits with User-level Differential Privacy

        The Pennsylvania State University

		

	

	

		FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization

        Shanghai Jiao Tong University

		code

	

	

		TabLeak: Tabular Data Leakage in Federated Learning

        ETH Zurich

		

	

	

		Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis

        Georgia Institute of Technology

		

	

	

		The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond

        Carnegie Mellon University

		

	

	

		LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning

        Rensselaer Polytechnic Institute

		

	

	

		One-Shot Federated Conformal Prediction

        Universite Paris-Saclay

		code

	

	

		Fast Federated Machine Unlearning with Nonlinear Functional Theory

        Auburn University

		code

	

	

		Efficient Personalized Federated Learning via Sparse Model-Adaptation

        Alibaba

		code

	

	

		DoCoFL: Downlink Compression for Cross-Device Federated Learning

        VMware Research

		

	

	

		Private Federated Learning with Autotuned Compression

        The Johns Hopkins University

		

	

	

		Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Linear Speedup and Partial Participation

        LinkedIn

		

	

	

		Personalized Subgraph Federated Learning

        KAIST

		code

	

	

		Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning

        Hong Kong University of Science and Technology 

		code

	

	

		Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape

        The University of Sydney

		

	

	

		Towards Understanding Ensemble Distillation in Federated Learning

        KAIST

		

	

	

		SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning

        Owkin Inc

		code

	

	

		FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization

        Alibaba

		code

	

	

		Federated Hypergradient Computation via Aggregated Iterative Differentiation

        University at Buffalo

		

	

	

		Personalized Federated Learning with Inferred Collaboration Graphs

        Shanghai Jiao Tong University

		code

	

	

		Secure Federated Correlation Test and Entropy Estimation

        Carnegie Mellon University

		code

	

	

		Doubly Adversarial Federated Bandits

        London School of Economics and Political Science

		

	

	

		FedDisco: Federated Learning with Discrepancy-Aware Collaboration

        Shanghai Jiao Tong University

		code

	

	

		Revisiting Weighted Aggregation in Federated Learning with Neural Networks

        Zhejiang University

		

	

	

		Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design

        Meta

		

	

	

		Anchor Sampling for Federated Learning with Partial Client Participation

        Purdue University

		code

	

	

		Federated Adversarial Learning: A Framework with Convergence Analysis

        University of British Columbia

		

	

	

		Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction

        Duke University

		

	

	

		GuardHFL: Privacy Guardian for Heterogeneous Federated Learning

        University of Electronic Science and Technology of China

		

	

	

		Vertical Federated Graph Neural Network for Recommender System

        National University of Singapore

		code

	

	

		FeDXL: Provable Federated Learning for Deep X-Risk Optimization

        Texas A&M University

		code

	

	

		Conformal Prediction for Federated Uncertainty Quantification Under Label Shift

        Lagrange Mathematics and Computing Research Center

		

	

	

		From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning

        Univ. Lille, Inria

		

	

	

		No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation

        Harbin Institute of Technology

		code

	

	

		Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships

        Jilin University

		code

	

	

		Federated Conformal Predictors for Distributed Uncertainty Quantification

        MIT

		code

	

	

		

		

		

        

	

	

        ICML 2022

        Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning

        Shanghai Jiao Tong University

		code
video

	

    

		Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization

        KAIST

		video
slide

	

	

		FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning

        University of Oulu

		code
video
slide

	

	

		FedNL: Making Newton-Type Methods Applicable to Federated Learning

        KAUST

		video
slide
video

	

	

		Federated Minimax Optimization: Improved Convergence Analyses and Algorithms

        Carnegie Mellon University

		slide
video

	

	

		FedNest: Federated Bilevel, Minimax, and Compositional Optimization

        University of Michigan

		code
video
slide

	

	

		Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification

        University of Maryland

		code
slide
video

	

	

		DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training

        University of Science and Technology of China

		code
video

	

	

		Federated Learning with Positive and Unlabeled Data

        Xi’an Jiaotong University

		video

	

	

		Neurotoxin: Durable Backdoors in Federated Learning

        Southeast University;
Princeton University

		code
slide
video

	

	

		DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning

        University of Cambridge

		slide
video

	

	

		Neural Tangent Kernel Empowered Federated Learning

        NC State University

		code
slide
video

	

	

		EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning

        VMware Research

		code
slide
video

	

	

		Architecture Agnostic Federated Learning for Neural Networks

        The University of Texas at Austin

		slide
video

	

	

		Fast Composite Optimization and Statistical Recovery in Federated Learning

        Shanghai Jiao Tong University

		slide
video

	

	

		Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning

        New York University

		slide
video

	

	

		Communication-Efficient Adaptive Federated Learning

        Pennsylvania State University

		slide
video

	

	

		Personalized Federated Learning via Variational Bayesian Inference

        Chinese Academy of Sciences

		code
video

	

	

		QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning

        Nankai University

		code
slide
video

	

	

		Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy

        University of Minnesota

		video

	

	

		The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation

        Stanford University; 
 Google Research

		slide
video

	

	

		The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning

        Stanford University; 
 Google Research

		code
video

	

	

		Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring

        University of Science and Technology of China

		slide
video

	

	

		Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling

        Geogia Institute of Technology

		slide
video

	

	

		Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering

        University of Michigan

		code
video

	

	

		Resilient and Communication Efficient Learning for Heterogeneous Federated Systems

        Michigan State University

		slide
video

	

	

		Accelerated Federated Learning with Decoupled Adaptive Optimization

        Auburn University

		slide
video

	

	

		Proximal and Federated Random Reshuffling

        KAUST

		code
video

	

	

		Personalized Federated Learning through Local Memorization

        Inria

		code
video

	

	

		Federated Learning with Partial Model Personalization

        University of Washington

		code
slide
video

	

	

		ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training

        CISPA Helmholz Center for Information Security

		code
video

	

	

		Federated Learning with Label Distribution Skew via Logits Calibration

        Zhejiang University

		slide
video

	

	

		Anarchic Federated Learning

        The Ohio State University

		slide
video

	

	

		Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning

        Hong Kong Baptist University

		code
video

	

	

		Generalized Federated Learning via Sharpness Aware Minimization

        University of South Florida

		slide
video

	

	

		FedScale: Benchmarking Model and System Performance of Federated Learning at Scale

        University of Michigan

		code
video

	

	

		Multi-Level Branched Regularization for Federated Learning

        Seoul National University

		HomePage
slide
video

	

	

		

		

		

        

	

	

        ICML 2021

        Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix

        Harvard University

		video
code

	

    

		FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis

        Peking University;
 Princeton University

		video

	

	

		Personalized Federated Learning using Hypernetworks

        Bar-Ilan University;
 NVIDIA 

		code
HomePage
video

	

	

		Federated Composite Optimization

        Stanford University;
 Google

		code
video
slides

	

	

		Exploiting Shared Representations for Personalized Federated Learning

        University of Texas at Austin;
 University of Pennsylvania

		code
video

	

	

		Data-Free Knowledge Distillation for Heterogeneous Federated Learning

        Michigan State University

		code
video

	

	

		Federated Continual Learning with Weighted Inter-client Transfer

        KAIST

		code
video

	

	

		Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity

        The University of Iowa

		video

	

	

		Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning

        The University of Tokyo

		video

	

	

		Federated Learning of User Verification Models Without Sharing Embeddings

        Qualcomm

		video

	

	

		Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning

        Accenture

		code
video

	

	

		Ditto: Fair and Robust Federated Learning Through Personalization

        CMU;
 Facebook AI

		code
video

	

	

		Heterogeneity for the Win: One-Shot Federated Clustering

        CMU

		video

	

	

		The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation

        Google

		video

	

	

        Debiasing Model Updates for Improving Personalized Federated Training

        Boston University;
 Arm 

		video

	

	

		One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning

        Toyota;
Berkeley;
 Cornell University

		code
video

	

	

        CRFL: Certifiably Robust Federated Learning against Backdoor Attacks

        UIUC;
IBM

        code
video

	

	

        Federated Learning under Arbitrary Communication Patterns

        Indiana University;
 Amazon

		video

	

	

		

		

		

        

	

	

        ICML 2020

		FedBoost: A Communication-Efficient Algorithm for Federated Learning

        Google

        Video

	

    

		FetchSGD: Communication-Efficient Federated Learning with Sketching

        UC Berkeley;
Johns Hopkins University;
Amazon

        Video
Code

	

    

		SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

        EPFL;
Google

        Video

	

    

		Federated Learning with

		Only Positive Labels

        Google

        Video

	

    

		From Local SGD to Local Fixed-Point Methods for Federated Learning

        Moscow Institute of Physics and Technology;
KAUST

        Slide
Video

	

    

		Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization

        KAUST

        Slide
Video

	

	

		

		

		

        

	

	

        ICML 2019

		Bayesian Nonparametric Federated Learning of Neural Networks

        IBM

        Code

	

    

		Analyzing Federated Learning through an Adversarial Lens

        Princeton University;
IBM

		Code

	

    

		Agnostic Federated Learning

        Google 

		

	



### ICLR



    

	

	

	

	

		Years

		Title

		Affiliation

		Materials

	

	

		ICLR 2023

		MocoSFL: enabling cross-client collaborative self-supervised learning

        Arizona State University

		Code

	

    

		Personalized Federated Learning with Feature Alignment and Classifier Collaboration 

        Tsinghua University

		

	

	

		Single-shot General Hyper-parameter Optimization for Federated Learning 

        IBM Research

		

	

	

		Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning

        Meta AI

		

	

	

		FedExP: Speeding Up Federated Averaging via Extrapolation

        Carnegie Mellon University

		code

	

	

		Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection

        Michigan State University

		code

	

	

		DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity

        KAUST

		code

	

	

		Federated Neural Bandits

        National University of Singapore

		code

	

	

		Machine Unlearning of Federated Clusters

        University of Illinois Urbana-Champaign

		code

	

	

		FedFA: Federated Feature Augmentation

        ETH Zurich

		code

	

	

		Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach

        Carnegie Mellon University

		code

	

	

		Better Generative Replay for Continual Federated Learning

        University of Virginia

		code

	

	

		Federated Learning from Small Datasets

        University Hospital Essen

		code

	

	

		Federated Nearest Neighbor Machine Translation

        University of Science and Technology of China

		code

	

	

		Test-Time Robust Personalization for Federated Learning

        Westlake University

		code

	

	

		DepthFL : Depthwise Federated Learning for Heterogeneous Clients

        Seoul National University

		

	

	

		Towards Addressing Label Skews in One-Shot Federated Learning

        National University of Singapore

		code

	

	

		Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning

        National University of Singapore

		code

	

	

		Bias Propagation in Federated Learning

        National University of Singapore

		code

	

	

		Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation

        University of Maryland

		

	

	

		SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication

        University of Maryland

		code

	

	

		Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses

        University of Southern California

		

	

	

		Effective passive membership inference attacks in federated learning against overparameterized models

        Purdue University

		

	

	

		FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy

        The University of Sydney

		

	

	

		FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification

        University of Cambridge

		code

	

	

		Multimodal Federated Learning via Contrastive Representation Ensemble

        Tsinghua University

		

	

	

		Faster federated optimization under second-order similarity

        Princeton University

		

	

	

		Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning

        ETH Zurich

		code

	

	

		The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation

        The University of Texas at Austin

		

	

	

		PerFedMask: Personalized Federated Learning with Optimized Masking Vectors

        The University of British Columbia

		code

	

	

		FedDAR: Federated Domain-Aware Representation Learning

        Harvard University

		code

	

	

		EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data

        George Mason University

		code

	

	

		FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning 

        Purdue University

		code

	

	

		Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses

        Renmin University of China

		

	

	

		Combating Exacerbated Heterogeneity for Robust Models in Federated Learning

        Hong Kong Baptist University

		code

	

	

		Efficient Federated Domain Translation

        Purdue University

		

	

	

		On the Importance and Applicability of Pre-Training for Federated Learning

        The Ohio State University

		code

	

	

		A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy

        University of California, Los Angeles

		

	

	

		Instance-wise Batch Label Restoration via Gradients in Federated Learning

        Beihang University

		code

	

	

		Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models

        University of Maryland

		

	

	

		Meta Knowledge Condensation for Federated Learning

        Center for Frontier AI Research

		

	

	

		Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity

        William & Mary

		

	

	

		CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning

        University of Warwick

		code

	

	

		Sparse Random Networks for Communication-Efficient Federated Learning

        Stanford University

		code

	

	

		Hyperparameter Optimization through Neural Network Partitioning

        University of Cambridge

		

	

	

		Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?

        MIT CSAIL

		code

	

	

		Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top

        KAUST

		

	

	

		

		

		

        

	

	

		ICLR 2022

		Bayesian Framework for Gradient Leakage

        ETH Zurich

		Code

	

    

		Federated Learning from only unlabeled data with class-conditional-sharing clients

        The University of Tokyo;
The Chinese University of Hong Kong

		Code

	

	

		FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning

        Carnegie Mellon University;
University of Illinois at Urbana-Champaign;
University of Washington

		

	

	

		Acceleration of Federated Learning with Alleviated Forgetting in Local Training

        Tsinghua University

		Code

	

	

		FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning

        POSTECH

		Code

	

	

		An Agnostic Approach to Federated Learning with Class Imbalance

        University of Pennsylvania 

		Code

	

	

		Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization

        Michigan State University;
The University of Texas at Austin

		code

	

	

		Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models

        University of Maryland;
New York University

		code (Minimum)
code (Comprehensive)

	

	

		ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity

        University of Cambridge;
University of Oxford

		

	

	

		Diverse Client Selection for Federated Learning via Submodular Maximization

        Intel;
Carnegie Mellon University

		code

	

	

		Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? 

        Purdue University

		code

	

	

		Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions 

        University of Maryland;
Google

		code

	

	

		Towards Model Agnostic Federated Learning Using Knowledge Distillation

        EPFL

		

	

	

		Divergence-aware Federated Self-Supervised Learning

         Nanyang Technological University;
SenseTime

		

	

	

		What Do We Mean by Generalization in Federated Learning? 

        Stanford University;
Google

		code

	

	

		FedBABU: Toward Enhanced Representation for Federated Image Classification 

        KAIST

		code

	

	

		Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing 

        EPFL

		code

	

	

		Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters

        Aibee

		code

	

	

		Hybrid Local SGD for Federated Learning with Heterogeneous Communications 

        University of Texas;
	Pennsylvania State University

		

	

	

		On Bridging Generic and Personalized Federated Learning for Image Classification 

        The Ohio State University

		code

	

	

		Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond

        KAIST;
MIT

		

	

	

		

		

		

        

	

	

		ICLR 2021

		Federated Learning Based on Dynamic Regularization

        Boston University;
ARM

		

	

    

		Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning

        The Ohio State University

		

	

	

		HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

        Duke University

		code

	

	

		FedMix: Approximation of Mixup under Mean Augmented Federated Learning

        KAIST

		

	

	

		Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms

        CMU; Google

		code

	

	

		Adaptive Federated Optimization

        Google

		code

	

	

		Personalized Federated Learning with First Order Model Optimization

        Stanford University; NVIDIA

		

	

	

		FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

        Princeton University

		code

	

	

		FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning

        The Ohio State University

		

	

	

		Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning

        KAIST

		code

	

	

		

		

		

        

	

	

		ICLR 2020

		Federated Adversarial Domain Adaptation

        Boston University;
Columbia University;
Rutgers University

		

	

    

		DBA: Distributed Backdoor Attacks against Federated Learning

        Zhejiang University;
IBM Research

        Code

	

    

		Fair Resource Allocation in Federated Learning

        CMU;
Facebook AI

        Code

	

    

		Federated Learning with Matched Averaging

        University of Wisconsin-Madison;
IBM Research

        Code

	

    

		Differentially Private Meta-Learning

        CMU

        

	

    

		Generative Models for Effective ML on Private, Decentralized Datasets

        Google

		Code

	

    

		On the Convergence of FedAvg on Non-IID Data

        Peking University

		Code

	



### NeurIPS



    

	

	

	

	

		Years

		Title

		Affiliation

		Materials

		

	

		NeurIPS 2023

		SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning

		University at Buffalo

		

	

	

		Multiply Robust Federated Estimation of Targeted Average Treatment Effects

		Northeastern University

		

	

	

		Incentivized Communication for Federated Bandits

		University of Virginia

		

	

	

		Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization

		Rutgers University

		

	

	

		Private Federated Frequency Estimation: Adapting to the Hardness of the Instance

		Johns Hopkins University

		

	

	

		Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition

		Mohamed Bin Zayed University of Artificial Intelligence

		code

	

	

		Convergence Analysis of Sequential Federated Learning on Heterogeneous Data

		Beijing University of Posts and Telecommunications

		code

	

	

		Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization

		University of Illinois, Urbana-Champaign

		code

	

	

		Federated Linear Bandits with Finite Adversarial Actions

		University of Virginia

		

	

	

		EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning

		KAIST

		

	

	

		IBA: Towards Irreversible Backdoor Attacks in Federated Learning

		Vanderbilt University

		code

	

	

		Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning

		University of Technology Sydney

		

	

	

		A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks

		University of Southern California

		code

	

	

		Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Federated Object Detection

		KAIST

		

	

	

		Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization

		Huazhong Agricultural University

		

	

	

		Guiding The Last Layer in Federated Learning with Pre-Trained Models

		Concordia University

		code

	

	

		FedNAR: Federated Optimization with Normalized Annealing Regularization

		Mohamed bin Zayed University of Artificial Intelligence

		code

	

	

		One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning

		Rice University

		code

	

	

		Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training

		Georgia Institute of Technology

		code

	

	

		FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning

		The Pennsylvania State University

		code

	

	

		Towards Personalized Federated Learning via Heterogeneous Model Reassembly

		The Pennsylvania State University

		code

	

	

		Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction

		The George Washington University

		

	

	

		DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning

		East China Normal University

		code

	

	

		A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning

		Western University

		code

	

	

		RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks

		Xidian University

		

	

	

		Federated Learning with Bilateral Curation for Partially Class-Disjoint Data

		Shanghai Jiao Tong University

		code

	

	

		Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds

		George Mason University

		code

	

	

		FedL2P: Federated Learning to Personalize

		University of Cambridge

		code

	

	

		Adaptive Test-Time Personalization for Federated Learning

		University of Illinois Urbana-Champaign

		code

	

	

		Federated Conditional Stochastic Optimization

		University of Pittsburgh

		

	

	

		Federated Spectral Clustering via Secure Similarity Reconstruction

		The Chinese University of Hong Kong

		

	

	

		Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM

		University of Michigan

		

	

	

		FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks

		Carnegie Mellon University

		code

	

	

		FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout

		University of British Columbia

		code

	

	

		Flow: Per-instance Personalized Federated Learning

		University of Massachusetts

		code

	

	

		A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning

		The Pennsylvania State University



		code

	

	

		Federated Compositional Deep AUC Maximization

		Temple University

		

	

	

		DELTA: Diverse Client Sampling for Fasting Federated Learning

		The Chinese University of Hong Kong

		code

	

	

		Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization

		The University of Sydney

		code

	

	

		StableFDG: Style and Attention Based Learning for Federated Domain Generalization

		KAIST

		

	

	

		Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems

		University of Pittsburgh

		

	

	

		Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning

		University of Pittsburgh

		

	

	

		Solving a Class of Non-Convex Minimax Optimization in Federated Learning

		University of Pittsburgh

		code

	

	

		Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning

		ShanghaiTech University

		code

	

	

		Dynamic Personalized Federated Learning with Adaptive Differential Privacy

		Wuhan University

		code

	

	

		Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer

		Zhejiang University

		code

	

	

		Structured Federated Learning through Clustered Additive Modeling

		University of Technology Sydney

		

	

	

		Federated Learning with Manifold Regularization and Normalized Update Reaggregation

		Beijing Institute of Technology

		

	

	

		Eliminating Domain Bias for Federated Learning in Representation Space

		Shanghai Jiao Tong University

		code

	

	

		Spectral Co-Distillation for Personalized Federated Learning

		Singapore University of Technology and Design

		code

	

	

		PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning

		Sichuan University

		code

	

	

		FedFed: Feature Distillation against Data Heterogeneity in Federated Learning

		Beihang University

		code

	

	

		Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense

		National Key Laboratory for Multimedia Information Processing

		code

	

	

		Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense

		National Key Laboratory for Multimedia Information Processing

		code

	

	

		SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning

		National Taiwan University

		code

	

	

		Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates

		Purdue University

		

	

	

		

		

		

        

	

	

		NeurIPS 2022

		Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

		University of Technology Sydney

		

	

	

		CalFAT: Calibrated Federated Adversarial Training with Label Skewness

		Zhejiang University

		

	

	

		DENSE: Data-Free One-Shot Federated Learning 

		Zhejiang University

		

	

	

		Federated Submodel Optimization for Hot and Cold Data Features

		Shanghai Jiao Tong University

		code

	

	

		SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression

		CMU

		code

	

	

		Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching

		KAIST

		

	

	

		FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction

		Michigan State University

		code

	

	

		A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits

		UCLA

		code

	

	

		Byzantine-tolerant federated Gaussian process regression for streaming data 

		Pennsylvania State University

		

	

	

		Preservation of the Global Knowledge by Not-True Distillation in Federated Learning

		KAIST

		

	

	

		TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels 

		UC Berkeley

		code

	

	

		A Unified Analysis of Federated Learning with Arbitrary Client Participation

		IBM

		code

	

	

		SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training

		Duke University

		code

	

	

		A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning

		Northwestern University

		

	

	

		Resource-Adaptive Federated Learning with All-In-One Neural Composition 

		Johns Hopkins University

		

	

	

		Fairness in Federated Learning via Core-Stability

		University of Illinois at Urbana Champaign

		code

	

	

		FedSR: A Simple and Effective Domain Generalization Method for Federated Learning

		University of Oxford

		code

	

	

		On Sample Optimality in Personalized Collaborative and Federated Learning

		Inria 

		

	

	

		Global Convergence of Federated Learning for Mixed Regression

		Northeastern University

		

	

	

		DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing 

		Hong Kong University of Science and Technology

		

	

	

		SAGDA: Achieving  Communication Complexity in Federated Min-Max Learning 

		The Ohio State University

		

	

	

		SAGDA: Achieving  Communication Complexity in Federated Min-Max Learning 

		The Ohio State University

		

	

	

		FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

		Tsinghua University

		code

	

	

		FedPop: A Bayesian Approach for Personalised Federated Learning

		Skolkovo Institute of Science and Technology

		code

	

	

		Self-Aware Personalized Federated Learning

		Amazon

		

	

	

		Recovering Private Text in Federated Learning of Language Models

		Princeton University

		code

	

	

		Communication Efficient Federated Learning for Generalized Linear Bandits 

		University of Virginia

		code

	

	

		A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning

		National University of Singapore

		code

	

	

		On Privacy and Personalization in Cross-Silo Federated Learning 

		CMU

		code

	

	

		Personalized Online Federated Learning with Multiple Kernels

		University of California Irvine

		code

	

	

		Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox

		KAUST

		

	

	

		Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework

		Tulane University

		code

	

	

		Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering

		Nanjing University

		code

	

	

		An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects

		National University of Singapore

		code

	

	

		LAMP: Extracting Text from Gradients with Language Model Priors

		ETH Zurich

		code

	

	

		SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning

		Owkin Inc

		

	

	

		VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?

		Wuhan University

		

	

	

		Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning 

		EPFL

		

	

	

		Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning 

		KAUST

		

	

	

		Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning

		The Ohio State University

		

	

	

		FedAvg with Fine Tuning: Local Updates Lead to Representation Learning

		The University of Texas at Austin

		

	

	

		Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness 

		Peking University

		code

	

	

		On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond

		Baidu Research

		

	

	

		Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams 

		University of Wisconsin-Madison

		

	

	

		Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks 

		HKUST

		

	

	

		Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective 

		Peking University

		

	

	

		Subspace Recovery from Heterogeneous Data with Non-isotropic Noise 

		Stanford University

		

	

	

		EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization

		KAUST

		

	

	

		On-Demand Sampling: Learning Optimally from Multiple Distributions

		University of California, Berkeley

		code

	

	

		Improved Utility Analysis of Private CountSketch 

		University of Copenhagen

		code

	

	

		Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning

		Huawei Technologies France

		code

	

	

		Decentralized Local Stochastic Extra-Gradient for Variational Inequalities

		Yandex

		code

	

	

		BEER: Fast  Rate for Decentralized Nonconvex Optimization with Communication Compression

		Princeton University

		code

	

	

		Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning

		NTT DATA Mathematical Systems Inc

		

	

	

		Near-Optimal Collaborative Learning in Bandits

		Université Paris Cité

		code

	

	

		Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees

		Yandex

		

	

	

		Towards Optimal Communication Complexity in Distributed Non-Convex Optimization

		TTIC

		code

	

	

		

		

		

        

	

    

        NeurIPS 2021

		Sageflow: Robust Federated Learning against Both Stragglers and Adversaries

        KAIST

        HomePage

	

    

		CAFE: Catastrophic Data Leakage in Vertical Federated Learning

        Rensselaer Polytechnic Institute;
IBM Research

        code
HomePage

	

    

        Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee

        NUS

        code
HomePage

	

    

		Optimality and Stability in Federated Learning: A Game-theoretic Approach

        Cornell University

        code
HomePage

	

    

		QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning

        UCLA

        HomePage

	

    

		The Skellam Mechanism for Differentially Private Federated Learning

        Google Research;
CMU

		HomePage

	

    

		No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

        NUS; 
 Huawei

		HomePage

	

    

		STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning

        University of Minnesota

        HomePage

	

    

		Subgraph Federated Learning with Missing Neighbor Generation

        Emory University; 
 University of British Columbia; 
 Lehigh University

		HomePage

	

    

		Evaluating Gradient Inversion Attacks and Defenses in Federated Learning

        Princeton University

		Code
HomePage

	

    

		Personalized Federated Learning With Gaussian Processes

        Bar-Ilan University

		code
HomePage

	

    

		Differentially Private Federated Bayesian Optimization with Distributed Exploration

        MIT;
NUS

		code
HomePage

	

    

		Parameterized Knowledge Transfer for Personalized Federated Learning

        Hong Kong Polytechnic University; 
 

        HomePage

	

    

		Federated Reconstruction: Partially Local Federated Learning

        Google Research

        HomePage

	

    

		Fast Federated Learning in the Presence of Arbitrary Device Unavailability

        Tsinghua University;
Princeton University;
MIT

        code
HomePage

	

    

		FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective

        Duke University;
Accenture Labs

        code
HomePage

	

    

		FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

        KAUST;
Samsung AI Center

		HomePage

	

    

		Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients

        University of Pennsylvania

		HomePage

	

    

		Federated Multi-Task Learning under a Mixture of Distributions

        INRIA;
Accenture Labs

        code
HomePage

	

    

		Federated Graph Classification over Non-IID Graphs

        Emory University

        HomePage

	

    

		Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing

        CMU;
Hewlett Packard Enterprise

        code
HomePage

	

    

		On Large-Cohort Training for Federated Learning

        Google;
CMU

        code
HomePage

	

    

		DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning

        KAUST;
Columbia University;
University of Central Florida

        code
HomePage

	

    

		PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization

        Huawei

        HomePage

	

    

		Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis

        KAIST

        HomePage

	

    

		Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning

        Tsinghua University;
 Alibaba;
Weill Cornell Medicine

        code
HomePage

	

    

		Federated Linear Contextual Bandits

        The Pennsylvania State University;
 Facebook;
University of Virginia

        HomePage

	

    

		Few-Round Learning for Federated Learning

        KAIST

        HomePage

	

    

		Breaking the centralized barrier for cross-device federated learning

        EPFL;
Google Research

        code
HomePage

	

    

		Federated-EM with heterogeneity mitigation and variance reduction

        Ecole Polytechnique;
Google Research

        HomePage

	

    

		Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning

        MIT;
Amazon;
Google

        HomePage

	

    

		FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization

        University of North Carolina at Chapel Hill;
IBM Research

        code
HomePage

	

    

		Gradient Inversion with Generative Image Prior

        Pohang University of Science and Technology;
University of Wisconsin-Madison;
University of Washington

        code
HomePage

	

	

		

		

		

        

	

	

        NeurIPS 2020

		Differentially-Private Federated Linear Bandits

        MIT

		code

	

    

		Federated Principal Component Analysis

        University of Cambridge;
Quine Technologies

		code

	

    

        FedSplit: an algorithmic framework for fast federated optimization

        UC Berkeley

		

	

    

		Federated Bayesian Optimization via Thompson Sampling

        NUS; MIT

		

	

    

		Lower Bounds and Optimal Algorithms for Personalized Federated Learning

        KAUST

		

	

    

		Robust Federated

		Learning: The Case of Affine Distribution Shifts

        UC Santa Barbara; MIT

		

	

    

		An Efficient Framework for Clustered Federated Learning

        UC Berkeley; DeepMind

		Code

	

    

		Distributionally Robust

		Federated Averaging

        Pennsylvania State University

        Code

	

    

		Personalized

		Federated Learning with Moreau Envelopes

        The University of Sydney

		code

	

    

		Personalized Federated

		Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach

        MIT; UT Austin

		

	

    

		Group Knowledge

		Transfer: Federated Learning of Large CNNs at the Edge

        University of Southern California

		code

	

    

		Tackling the Objective

		Inconsistency Problem in Heterogeneous Federated Optimization

        CMU;
Princeton University

		

	

    

		Attack of the Tails:

		Yes, You Really Can Backdoor Federated Learning

        University of Wisconsin-Madison

		

	

    

		Federated Accelerated

		Stochastic Gradient Descent

        Stanford University

		code

	

    

		Inverting Gradients -

		How easy is it to break privacy in federated learning?

        University of Siegen

		code

	

    

		Ensemble Distillation for Robust Model Fusion in Federated Learning

        EPFL

		

	

    

		Throughput-Optimal Topology Design for Cross-Silo Federated Learning

        INRIA

		code

	

	

		

		

		

        

	

	

        NeurIPS 2018

		cpSGD: Communication-efficient and differentially-private distributed SGD

		Princeton University;
Google

		

	

	

		

		

		

        

	

	

        NeurIPS 2017

		Federated Multi-Task Learning

		Stanford; 
 USC; 
 CMU

		code

	



  



## Top Computer Vision Conferences

In this section, we will summarize Federated Learning papers accepted by top computer vision conference, Including CVPR, ICCV, ECCV.



### CVPR 



    

	

	

	

	

		Years

		Title

		Affiliation

		Materials

		

	

		CVPR 2023

		STDLens: Model Hijacking-Resilient Federated Learning for Object Detection 

        Georgia Instutite of Technology

		code

	

    

		On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data

        Technical University of Denmark

		code

	

	

		GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting

        East China Normal University

		code

	

	

		Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization 

        ESAT-PSI, KU Leuven

		code

	

	

		Elastic Aggregation for Federated Optimization

         Meituan

		

	

	

		Federated Learning With Data-Agnostic Distribution Fusion

        Nanjing University

		code

	

	

		How To Prevent the Poor Performance Clients for Personalized Federated Learning?

        Central South University

		

	

	

		FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning

        UCLA

		

	

	

		DaFKD: Domain-Aware Federated Knowledge Distillation

        Huazhong University of Science and Technology

		code

	

	

		Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity

        University of Macau

		

	

	

		ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients 

        Georgia Institute of Technology

		code

	

	

		The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning

        Purdue University

		

	

	

		Re-Thinking Federated Active Learning Based on Inter-Class Diversity

        KAIST

		code

	

	

		Rethinking Federated Learning With Domain Shift: A Prototype View

        Wuhan University

		code

	

	

		Reliable and Interpretable Personalized Federated Learning 

        Tianjin University

		

	

	

		Make Landscape Flatter in Differentially Private Federated Learning 

        Tsinghua University

		code

	

	

		Federated Incremental Semantic Segmentation

        Shenyang Institute of Automation

		code

	

	

		Federated Domain Generalization with Generalization Adjustment

        Shanghai Jiao Tong University

		code

	

	

		FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation

        Zhejiang University

		

	

	

		Bias-Eliminating Augmentation Learning for Debiased Federated Learning

        National Taiwan University

		

	

	

		Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning

        East China Normal University

		code

	

	

		Learning Federated Visual Prompt in Null Space for MRI Reconstruction

        IHPC

		code

	

	

		Fair Federated Medical Image Segmentation via Client Contribution Estimation

        The Chinese University of Hong Kong

		HomePage

	

	

		Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack

        Tsinghua University

		code

		

	

		

		

		

        

	

	

		CVPR 2022

		FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction

        National University of Defense Technology

		code

	

    

		Federated Class-Incremental Learning

        Chinese Academy of Sciences;
Northwestern University; 
University of Technology Sydney

		code

	

    

        Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning

         Peking University; 
 JD Explore Academy; 
 The University of Sydney

		

	

    

		Differentially Private Federated Learning with Local Regularization and Sparsification

        Chinese Academy of Sciences

		

	

	

		Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage

        University of Tennessee;
Oak Ridge National Laboratory;
Google Research

		code
video

	

	

		FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning

        Duke University

		

	

	

		Learn from Others and Be Yourself in Heterogeneous Federated Learning

        Wuhan University

		code
video

	

	

		ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning

        Arizona State University

		code

	

	

		Robust Federated Learning with Noisy and Heterogeneous Clients

        Wuhan University

		code

	

	

		Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

        Stanford University

		video
code

	

	

		CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning

        Shanghai Jiao Tong University

		

	

	

		FedCorr: Multi-Stage Federated Learning for Label Noise Correction

        Singapore University of Technology and Design

		video
code

	

	

		ATPFL: Automatic Trajectory Prediction Model Design under Federated Learning Framework

        Harbin Institute of Technology

		

	

	

		Federated Learning with Position-Aware Neurons

        Nanjing University

		

	

	

		RSCFed: Random Sampling Consensus Federated Semi-supervised Learning

        The Hong Kong University of Science and Technology

		code

	

	

		Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation

        Univ. of Pittsburgh;
NVIDIA

		code

	

	

		Layer-wised Model Aggregation for Personalized Federated Learning

        The Hong Kong Polytechnic University

		

	

	

		Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning

         University of Central Florida

		code

	

	

		

		

		

        

	

    

		CVPR 2021

		Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning

        Johns Hopkins University

		code

	

    

		Model-Contrastive Federated Learning

        National University of Singapore;
UC Berkeley

		code

	

    

        FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

        The Chinese University of Hong Kong

		code

	

    

		Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective

         Duke University

		code

	



### ECCV 



    

	

	

	

	

		Years

		Title

		Affiliation

		Materials

		

	

		ECCV 2022

		Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

        Johns Hopkins University

		code

	

	

		Personalizing Federated Medical Image

Segmentation via Local Calibration

        Xiamen Universit

		code

	

	

		SphereFed: Hyperspherical Federated Learning

        Harvard University

		

	

	

		FedX: Unsupervised Federated Learning

with Cross Knowledge Distillation

        KAIST

		code

	

	

		FedVLN: Privacy-preserving Federated

Vision-and-Language Navigation

        University of California

		code

	

	

		Addressing Heterogeneity in Federated Learning

via Distributional Transformation

        Johns Hopkins University

		code

	

	

		FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks

        MIT

		

	

	

		Improving Generalization in Federated Learning

by Seeking Flat Minima

        Politecnico di Torino

		code

	

	

		Federated Self-supervised Learning for Video Understanding

        TCL AI Lab

		code

	

	

		AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation

        Dalhousie University

		

	

	

		

		

		

        

	

    

		ECCV 2020

		Federated Visual Classification with Real-World Data Distribution

        MIT;
Google

		Video

	



### ICCV 



    

	

	

	

	

		Years

		Title

		Affiliation

		Materials

		

    

		ICCV 2021

		Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment

        Peking University

		

	

	

		Ensemble Attention Distillation for Privacy-Preserving Federated Learning

        University at Buffalo

		

	

	

		Collaborative Unsupervised Visual Representation Learning from Decentralized Data

        Nanyang Technological University;
SenseTime

		

	



  



## Top Artificial Intelligence and Data Mining Conferences



In this section, we will summarize Federated Learning papers accepted by top AI and DM conference, Including AAAI, AISTATS, KDD.



### AAAI



    

	

	

	

	

		Years

		Title

		Affiliation

		Materials

	

	

        AAAI 2023

		Win-Win: A Privacy-Preserving Federated Framework for Dual-Target Cross-Domain Recommendation

         Chinese Academy of Sciences

        

	

	

		Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense

        University of Science and Technology of China

        code

	

	

		Incentive-Boosted Federated Crowdsourcing

        Shandong University

        code

	

	

		Tackling Data Heterogeneity in Federated Learning with Class Prototypes

        Lehigh University

        code

	

	

		FairFed: Enabling Group Fairness in Federated Learning

        University of Southern California

        

	

	

		Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning

        Michigan State University

        code

	

	

		Complement Sparsification: Low-Overhead Model Pruning for Federated Learning

        New Jersey Institute of Technology

        

	

	

		Almost Cost-Free Communication in Federated Best Arm Identification

        National University of Singapore

        

	

	

		Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning

        University of Southern California

        

	

	

		Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning

        Beijing Jiaotong University

        

	

	

		FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance

        The Chinese University of Hong Kong

        

	

	

		Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning

        University of Southern California

        

	

	

		Federated Learning on Non-IID Graphs via Structural Knowledge Sharing

        University of Technology Sydney

        code

	

	

		Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces

        UC San Diego

        code

	

	

		FedABC: Targeting Fair Competition in Personalized Federated Learning

        Wuhan University

        

	

	

		Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework

        Singapore University of Technology and Design

        

	

	

		FedGS: Federated Graph-Based Sampling with Arbitrary Client Availability

        Xiamen University

        code

	

	

		Faster Adaptive Federated Learning

        University of Pittsburgh

        

	

	

		FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation

        The Hong Kong University of Science and Technology

        code

	

	

		Bayesian Federated Neural Matching That Completes Full Information

        Tongji University

        code

	

	

		CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems

        Zhejiang University

        code

	

	

		Federated Generative Model on Multi-Source Heterogeneous Data in IoT

        Georgia State University

        

	

	

		DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness

        SUNY-Binghamton University

        

	

	

		FedALA: Adaptive Local Aggregation for Personalized Federated Learning

        Shanghai Jiao Tong University

        code

	

	

		Delving into the Adversarial Robustness of Federated Learning

        Zhejiang University

        

	

	

		On the Vulnerability of Backdoor Defenses for Federated Learning

        Tongji University

        

	

	

		Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

        Renmin University of China

        

	

	

		DPAUC: Differentially Private AUC Computation in Federated Learning

        ByteDance

        code

	

	

		

		

		

        

	

	

        AAAI 2022

		HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

         The Chinese University of Hong Kong;
Beihang University

        code

	

	

		Cross-Modal Federated Human Activity Recognition via Modality-Agnostic and Modality-Specific Representation Learning

        Chinese Academy of Sciences

        

	

	

		FedInv: Byzantine-Robust Federated Learning by Inversing Local Model Updates

        Nanjing University of Aeronautics and Astronautics

        

	

	

		Learning Advanced Client Selection Strategy for Federated Learning

        Harvard University

        

	

    

		Federated Learning for Face Recognition with Gradient Correction

        Beijing University of Posts and Telecommunications

        

	

    

		SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data

        university of Southern California

        code

	

    

		SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures

        Harbin Institute of Technology;
Peng Cheng Laboratory

        

	

    

		Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning

        IIT Bombay

        

	

    

		Seizing Critical Learning Periods in Federated Learning

		SUNY-Binghamton University;
Louisiana State University

        

	

    

		Coordinating Momenta for Cross-silo Federated Learning

        University of Pittsburgh

        

	

    

		FedProto: Federated Prototype Learning over Heterogeneous Devices

        University of Technology Sydney;
University of Washington

        code

	

    

		FedSoft: Soft Clustered Federated Learning with Proximal Local Updating

        Carnegie Mellon University

        

	

    

		Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better

        The University of Texas at Austin

        code

	

    

		FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition

        National Taiwan University

        code

	

    

		SplitFed: When Federated Learning Meets Split Learning

        CSIRO;
Lehigh University

        code

	

    

		Efficient Device Scheduling with Multi-Job Federated Learning

        Soochow University;
Baidu

        

	

    

		Implicit Gradient Alignment in Distributed and Federated Learning

        IIT Kanpur;
EPFL

        

	

	

		Federated Nearest Neighbor Classification with a Colony of Fruit-Flies

        IBM Research;
Wichita State University

        code

	

	

		

		

		

        

	

	

        AAAI 2021

		Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating

         Xidian University;
JD Tech

        video

	

    

		 FedRec++: Lossless Federated Recommendation with Explicit Feedback

        Shenzhen University

        video

	

    

		Federated Multi-Armed Bandits

        University of Virginia

        code
video

	

    

		On the Convergence of Communication-Efficient Local SGD for Federated Learning

        Temple University;
University of Pittsburgh

        video

	

    

		FLAME: Differentially Private Federated Learning in the Shuffle Model

        Renmin University of China;
Kyoto University

        video
code

	

    

		Toward Understanding the Influence of Individual Clients in Federated Learning

		Shanghai Jiao Tong University;
The University of Texas at Dallas

        video

	

    

		Provably Secure Federated Learning against Malicious Clients

        Duke University

        video
slides

	

    

		Personalized Cross-Silo Federated Learning on Non-IID Data

        Simon Fraser University;
McMaster University

        video

	

    

		Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation

        Cornell University

        code
video

	

    

		Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning

        University of Nevada;
IBM Research

        video

	

    

		Game of Gradients: Mitigating Irrelevant Clients in Federated Learning

        IIT Bombay;
IBM Research

        video
Supplementary

	

    

		Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models

        The Chinese University of Hong Kong;
Arizona State University

        video
code

	

    

		Adressing Class Imbalance in Federated Learning

        Northwestern University

        video
code

	

    

		Defending against Backdoors in Federated Learning with Robust Learning Rate

        The University of Texas at Dallas

        video
code

	

	

		

		

		

        

	

	

        AAAI 2020

		 Practical Federated Gradient Boosting Decision Trees

        National University of Singapore;
 The University of Western Australia

        code

	

    

		Federated Learning for Vision-and-Language Grounding Problems

        Peking University;
Tencent

        

	

    

		Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework

        Beihang University

        

	

    

		Federated Patient Hashing

        Cornell University

        

	

    

		Robust Federated Learning via Collaborative Machine Teaching

        Symantec Research Labs;
KAUST

        

	



### AISTATS



    

	

	

	

	

		Years

		Title

		Affiliation

		Materials

		

	

        AISTATS 2022

		Federated Reinforcement Learning with Environment Heterogeneity

        Peking University

        code

	

    

		Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning

        KAUST

        

	

    

		Federated Learning with Buffered Asynchronous Aggregation

        Meta AI

        video

	

    

		Federated Myopic Community Detection with One-shot Communication

        Purdue University

        

	

    

		QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning

        Criteo AI Lab

        video
code

	

    

		Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits

        University of Virginia

        code

	

    

		SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification

        Princeton University

        video
code

	

	

		Federated Functional Gradient Boosting

        University of Pennsylvania

        code

	

	

		Towards Federated Bayesian Network Structure Learning with Continuous Optimization

        Carnegie Mellon University

        code

	

	

		Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective

        Stanford University

        code

	

	

		Differentially Private Federated Learning on Heterogeneous Data

        Stanford University

        code

	

	

		Towards Understanding Biased Client Selection in Federated Learning

        Carnegie Mellon University

        code

	

	

		FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning

        KAUST

        code

	

	

		

		

		

        

	

	

        AISTATS 2021

		Free-rider Attacks on Model Aggregation in Federated Learning

        Accenture Labs

        video
Supplementary

	

    

		Federated f-differential privacy

        University of Pennsylvania

        code
video
Supplementary

	

    

		Federated learning with compression: Unified analysis and sharp guarantees

        The Pennsylvania State University;
The University of Texas at Austin

        code
video
Supplementary

	

    

		Shuffled Model of Differential Privacy in Federated Learning

        UCLA;
Google

        video
Supplementary

	

    

		Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning

        Google

        video
Supplementary

	

    

		Federated Multi-armed Bandits with Personalization

		University of Virginia;
The Pennsylvania State University

        code
video
Supplementary

	

    

		Towards Flexible Device Participation in Federated Learning

        CMU;
 Sun Yat-Sen University

        video
Supplementary

	

	

		

		

		

        

	

	

        AISTATS 2020

		FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

        UC Santa Barbara;
 UT Austin

        video
Supplementary

	

    

		How To Backdoor Federated Learning

		Cornell Tech

        video
code
Supplementary

	

    

		Federated Heavy Hitters Discovery with Differential Privacy

        RPI;
Google

        video
Supplementary

	



### KDD



    

	

	

	

	

		Years

        Sessions

		Title

		Affiliation

		Materials

		

	

        KDD 2023

        Research Track

		FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity

        Zhejiang University

        code

	

    

		DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization

        Beihang University

        code

	

    

		FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy

        Shanghai Jiao Tong University

        code

	

    

		Personalized Federated Learning with Parameter Propagation

        UniversityofIllinoisatUrbana-Champaign

        

	

	

		Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework

        University of California, San Diego

        code

	

	

		CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning

        State University of New York, Binghamton

        

	

	

		Federated Few-shot Learning

        University of Virginia

        code

	

	

		FedDefender: Client-Side Attack-Tolerant Federated Learning

        KAIST

        code

	

	

		Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining

        University of Pittsburgh

        code

	

	

		Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity

        Shandong University

        code

	

	

		ShapleyFL: Robust Federated Learning Based on Shapley Value

        Zhejiang University

        code

	

	

		FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis

        University of Maryland, Baltimore County

        code

	

	

		Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation

        University of Cambridge

        code

	

	

		FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework

        L3S Research Center

        

	

	

        Applied Data Science Track

		FedMultimodal: A Benchmark for Multimodal Federated Learning

        University of Southern California

        code

	

	

		UA-FedRec: Untargeted Attack on Federated News Recommendation

        University of Science and Technology of China

        code

	

	

		PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation

        Renmin University of China

        

	

	

		FS-REAL: Towards Real-World Cross-Device Federated Learning

        Alibaba Group

        code

	

	

		Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks

        Hong Kong University of Science and Technology

        code

	

	

		

		

		

        

	

	

        KDD 2022

        Research Track

		Collaboration Equilibrium in Federated Learning



        Tsinghua University;
Alibaba Group

        code

	

    

		Connected Low-Loss Subspace Learning for a Personalization in Federated Learning

        Ulsan National Institute of Science and Technology & Kakao Enterprise

        code

	

    

		Communication-Efficient Robust Federated Learning with Noisy Labels

        University of Pittsburgh

        

	

    

		FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks

        

        

	

    

        Application Track

		Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch

        Beihang University

        

	

    

		No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices

        Renmin University of China

        

	

	

		EasyFGL: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning

        Alibaba Group

        code

	

	

		FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling

        Tsinghua University

        code

	

	

		

		

		

        

	

	

        KDD 2021

        Research Track

		Fed2: Feature-Aligned Federated Learning



        George Mason University;
Microsoft;
University of Maryland

        

	

    

		FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data

        Nanjing University

        code

	

    

		Federated Adversarial Debiasing for Fair and Trasnferable Representations

        Michigan State University

        HomePage

	

    

		Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling

        University of Southern California

        code

	

    

        Application Track

		AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization

        JD Tech

        

	

    

		FLOP: Federated Learning on Medical Datasets using Partial Networks

        Duke University

        code

	

	

		

		

		

        

	

	

            KDD 2020

        Research Track

		FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems

        University College Dublin

        video

	

    

        Application Track

		Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data

        JD Tech

        video

	



  



## Books



- 联邦学习（Federated Learning）



  [Chinese Version](https://item.jd.com/12649191.html)



  [English Version](https://www.amazon.com/Federated-Learning-Synthesis-Artificial-Intelligence/dp/1681736977/ref=sr_1_1?dchild=1&keywords=federated+learning&qid=1617695403&sr=8-1)



- 联邦学习实战（Practicing Federated Learning）



  [Chinese Version](https://item.jd.com/13206070.html)

  

  [Github](https://github.com/FederatedAI/Practicing-Federated-Learning)

  

- Federated Learning - A Comprehensive Overview of Methods and Applications



  [English Version](https://link.springer.com/book/10.1007/978-3-030-96896-0)

  



## Papers



### 1. Model Aggregation



Model Aggregation (or Model Fusion) refers to how to combine local models into a shared global model.  



    

	

	

	

	

		Papers

		Abbreviation

		Conferences/Affiliations

		Materials

		

    

		Communication-Efficient Learning of Deep Networks from Decentralized Data

		FedAvg

        ASTATS 2017

		

	

	

		A bayesian federated learning framework with online laplace approximation

		FedBayes

        TPAMI

		code

	

	

		Bayesian Nonparametric Federated Learning of Neural Networks

		PFNM

        ICML 2019

		code

	

	

		Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent

		Krum

        NeurIPS 2017

		

	

	

		Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

		median;
trimmed mean

        ICML 2018

		

	

	

		Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms

		median;
mean

        NeurIPS 2020

		

	

	

		The hidden vulnerability of distributed learning in byzantium

		Bulyan

        ICML 2018

		

	

	

		Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance

		Zeno 

        ICML 2019

		code

	

	

		Statistical Model Aggregation via Parameter Matching

		SPAHM

        	NeurIPS 2019

		code

	

	

		Fed+: A Unified Approach to Robust Personalized Federated Learning

		Fed+

        

		

	

	

		FEDERATED OPTIMIZATION IN HETEROGENEOUS NETWORKS

		FedProx

        MLSys 2020

		code

	

	

		Separation of Powers in Federated Learning

		Truda

        

		

	

	

		FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning

		FedBE

        ICLR 2021
(The Ohio State University)

		

	

	

		Federated Learning with Matched Averaging

		FedMA

        ICLR 2020
(University of Wisconsin-Madison; IBM)

        Code

	

	

		FedSim: Similarity Guided Model Aggregation for Federated Learning

		FedSim

        Neurocomputing 2022 (Vol 483)
(Robert Gordon University, UK)

        Code

	

	

		Model Fusion via Optimal Transport



		

        NeurIPS 2020
(ETH, EPFL)

        Code

	



  



### 2. Personalization



Personalized federated learning refers to train a model for each client, based on the client’s own dataset and the datasets of other clients. There are two major motivations for personalized federated learning：



- Due to statistical heterogeneity across clients, a single global model would not be a good choice for all clients. Sometimes, the local models trained solely on their private data perform better than the global shared model.   

- Different clients need models specifically customized to their own environment. As an example of model heterogeneity, consider the sentence: “I live in .....”. The next-word prediction task applied on this sentence needs to predict a different answer customized for each user. Different clients may assign different labels to the same data.



Personalized federated learning Survey paper:



- [Survey of Personalization Techniques for Federated Learning](https://arxiv.org/pdf/2003.08673.pdf)



- [Three Approaches for Personalization with Applications to Federated Learning](https://arxiv.org/pdf/2002.10619.pdf)



- [Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework](https://arxiv.org/pdf/2002.10671.pdf)



  



    

	

	

	

		Methodology

		Papers

		Conferences/Affiliations

		Materials

	

	

    	Multi-Task Learning

    	Federated Multi-Task Learning

        NeurIPS 2017
(Stanford; USC; CMU)

        code

    

	

    	Decentralized Collaborative Learning of Personalized Models over Networks

        AISTATS 2017
(INRIA)

        

    

    

    	Variational Federated Multi-Task Learning

        ETH Zurich

        

    

	

    	Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs

        AISTATS 2020
(INRIA)

        video

    

	

    	Personalized Cross-Silo Federated Learning on Non-IID Data

        AAAI 2021
(Simon Fraser University; McMaster University; Huawei Technologies Canada)

        video

    

	

    	Ditto: Fair and Robust Federated Learning Through Personalization

        ICML 2021
(CMU; Facebook AI)

        code
video

    

	

    	Federated Multi-Task Learning under a Mixture of Distributions

        NeurIPS 2021
(Inria; Accenture Labs)

        code

    

    

    	

    	

    	

        

    

    

    	Meta Learning

    	Personalized Federated Learning: A Meta-Learning Approach

        MIT

        

    

	

        Debiasing Model Updates for Improving Personalized Federated Training

        ICML 2021
(Boston University; Arm) 

		video

	

    

    	Improving Federated Learning Personalization via Model Agnostic Meta Learning

        University of Washington; Google

        

    

    

    	Adaptive Gradient-Based Meta-Learning Methods

        CMU

        

    

    

    	Federated Meta-Learning with Fast Convergence and Efficient Communication

        Huawei Noah’s Ark Lab

        

    

    

    	

    	

    	

        

    

    

    	Mixture of Global and Local Models

    	Federated Learning of a Mixture of Global and Local Models

        KAUST

        

    

    

    	Federated User Representation Learning

        University of Michigan
Facebook

        

    

    

    	Adaptive Personalized Federated Learning

        The Pennsylvania State University

        

    

    

    	

    	

    	

        

    

    

    	 Personalization Layers

    	Federated Learning with Personalization Layers

        Adobe Research
Indian Institute of Technology

        

    

    

    	Think Locally, Act Globally: Federated Learning with Local and Global Representations

        CMU
University of Tokyo
Columbia University

        

	

	

		Exploiting Shared Representations for Personalized Federated Learning

        ICML 2021
(University of Texas at Austin;
 University of Pennsylvania)

        code
video

	

	

		

		

		

        

	

	

		Transfer Learning

		Federated evaluation of on-device personalization

        Google

        

	

    

		Salvaging Federated Learning by Local Adaptation

        Cornell University

        

	

	

		Private Federated Learning with Domain Adaptation



        Oracle Labs

        

	

	

		QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning

        NeurIPS 2021
(UCLA)

        HomePage

	

	

		

		

		

        

	

	

		Clustering

		Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints

        Fraunhofer Heinrich Hertz Institute

        Code

	

    

		An Efficient Framework for Clustered Federated Learning

        UC Berkeley
DeepMind

        Code

	

	

		Robust Federated Learning in a Heterogeneous Environment

        UC Berkeley

        

	

	

		Personalized Federated Learning with First Order Model Optimization

        ICLR 2021
(Stanford University; NVIDIA)

		

	

	

		

		

		

        

	

	

		Hypernetwork

		Personalized Federated Learning using Hypernetworks

        Bar-Ilan University;
 NVIDIA 

        code
HomePage
video

	



  



### **3. Recommender system**



Recommender system (RecSys) is widely used to solve information overload. In general, the more data RecSys use, the better the recommendation performance we can obtain. 



Traditionally, RecSys requires the data that are distributed across multiple devices to be uploaded to the central database for model training. However, due to privacy and security concerns, such directly sharing user data strategies are no longer appropriate. 



The incorporation of federated learning and RecSys is a promising approach, which can alleviate the risk of privacy leakage.



More federated recommendation papers can be found in this repository: [FedRecPapers](https://github.com/AustinNeverPee/FedRecPapers)



    

	

	

	

		Methodology

		Papers

		Conferences/Affiliations

		Materials

	

	

    	Matrix Factorization

    	Secure federated matrix factorization

        IEEE Intelligent Systems

        

    

    

    	Federated Multi-view Matrix Factorization for Personalized Recommendations

        ECML-PKDD 2020

        video

    

    

    	Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning

        ECML-PKDD 2019

        

    

    

    	Towards Privacy-preserving Mobile Applications with Federated Learning: The Case of Matrix Factorization

        MobiSys 2019

        

    

    

    	Meta Matrix Factorization for Federated Rating Predictions

        ACM SIGIR 2020

        code

    

    

    	Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

        Arxiv

        

    

    

    	

    	

    	

        

    

    

    	GNN

    	FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

        Arxiv

        

    

	

    	Federated Social Recommendation with Graph Neural Network

        ACM TIST

        

    

	

    	Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation

        CIKM 2022

        

    

	

    	Federated Meta Embedding Concept Stock Recommendation

        IEEE Big Data 2022

        

    

	

    	Federated Learning-Based Cross-Enterprise Recommendation With Graph Neural Networks

        IEEE Transactions on Industrial Informatics

        

    



  



### **4. Security**



#### 4.1. Attack



    

	

	

	

		Methodology

		Papers

		Conferences/Affiliations

		Materials

	

	

    	Backdoor Attack

    	How To Backdoor Federated Learning

        AISTATS 2020

    	code

    

    

    	Can You Really Backdoor Federated Learning?

        Arxiv

        

    

    

    	Attack of the Tails: Yes, You Really Can Backdoor Federated Learning

        NeurIPS 2020

        code

    

    

    	DBA: Distributed Backdoor Attacks against Federated Learning

        ICLR 2020

        code

    

	

    	

    	

    	

        

    

	

    	Gradients Attack

    	Deep Leakage from Gradients

        NeurIPS 2020

    	HomePage
code

    

	

    	Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix

        ICML 2021

        code

    

	

    	Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

        ACM CCS 2017

        video

    

	

    	iDLG: Improved Deep Leakage from Gradients

        Arxiv

        code

    

	

    	See through Gradients: Image Batch Recovery via GradInversion

        CVPR 2021

        

    

	

    	Inverting Gradients - How easy is it to break Privacy in Federated Learning?

        NeurIPS 2020

        code

    

	

		CAFE: Catastrophic Data Leakage in Vertical Federated Learning

        NeurIPS 2021
(Rensselaer Polytechnic Institute; IBM)

        code
HomePage

	

	

		Evaluating Gradient Inversion Attacks and Defenses in Federated Learning

        NeurIPS 2021
(Princeton University)

		

	

	

		R-GAP: RECURSIVE GRADIENT ATTACK ON PRIVACY

        ICLR 2021
(KU Leuven, Belgium)

		Code

	

	

		Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage

        CVPR 2022
(University of Tennessee;Oak Ridge National Laboratory)

		Code

	

	

    	

    	

    	

        

    

	

    	Model Poison Attack

    	Analyzing Federated Learning through an Adversarial Lens

        ICML 2019
(Princeton University; IBM)

		Code

    

	

		Turning Privacy-preserving Mechanisms against Federated Learning

        CCS 2023
(University of Pavia; TU Delft; University of Padua; Radboud University)

		Code

	



  



#### 4.2. Defense



    

	

	

	

	

		Methodology

		Papers

		Conferences/Affiliations

		Materials

	

	

    	FL+DP

    	Federated Learning With Differential Privacy: Algorithms and Performance Analysis

        IEEE Transactions on Information Forensics and Security

    	

    

    

    	Differentially Private Federated Learning: A Client Level Perspective

        Arxiv

        code

    

    

    	Learning Differentially Private Recurrent Language Models

        ICLR 2018

        

    

	

		The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation

        ICML 2021
(Google)

		video

	

	

		The Skellam Mechanism for Differentially Private Federated Learning

        NeurIPS 2021
(Google Research; CMU)

		HomePage

	

    

    	

    	

    	

        

    

    

    	FL+HE

    	Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

        Arxiv

        

    

	

    	BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning

        USENIX 2020

        code

    

    

    	

    	

    	

        

    

	

    	FL+TEE

    	PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments

        ACM MobiSys 2021

        

    

	

    	Darknetz: towards model privacy at the edge using trusted execution environments.

        ACM MobiSys 2020

        code
video

    

	

    	

    	

    	

        

    

    

    	Algorithm

    	A Little Is Enough: Circumventing Defenses For Distributed Learning

        NeurIPS 2019

        

    

    

    	CRFL: Certifiably Robust Federated Learning against Backdoor Attacks

        ICML 2021
(UIUC; IBM)

        code
video

    

	

		Evaluating Gradient Inversion Attacks and Defenses in Federated Learning

        NeurIPS 2021
(Princeton University)

		Code
HomePage

	

    



  



### **5. Survey**



    

	

	

	

		Category

		Papers

	

	

    	General

    	Federated Learning with Privacy-preserving and Model IP-right-protection

    

	

    	Federated machine learning: Concept and applications

    

    

    	A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

    

	

    	Federated Learning in Mobile Edge Networks: A Comprehensive Survey

    

	

    	Advances and Open Problems in Federated Learning

    

	

    	Federated Learning: Challenges, Methods, and Future Directions

    

	

    	The Internet of Federated Things (IoFT)

    

	

    	Vertical Federated Learning: Challenges, Methodologies and Experiments

    

	

    	Vertical Federated Learning

    

    

    	

    	

    

    

    	Security

    	A survey on security and privacy of federated learning

    

    

    	Threats to Federated Learning: A Survey

    

    

    	Vulnerabilities in Federated Learning

    

    

    	

    	

    

	

    	Personalization

    	Survey of Personalization Techniques for Federated Learning

    

    

    	Towards Personalized Federated Learning

    

	

    	

    	

    

	

    	Aggregation

    	Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

    

	

    	

    	

    

	

    	Incentive

    	A Comprehensive Survey of Incentive Mechanism for Federated Learning

    

	

    	A Survey of Incentive Mechanism Design for Federated Learning

    

	

    	Incentive Mechanisms for Federated Learning:

From Economic and Game Theoretic Perspective

    

	

    	

    	

    

	

    	Applications

    	A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things

    

	

    	Applications

    	Privacy Computing Meets Metaverse: Necessity, Taxonomy and Challenges

    

	

    	

    	

    

	

    	Fairness

    	A Survey of Fairness-Aware Federated Learning

    

	

    	

    	

    

	

    	Graph

    	FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

    

	

    	Federated Graph Learning - A Position Paper

    

	

    	Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications

    

	

    	Federated Graph Neural Networks: Overview, Techniques and Challenges

    

	

    	

    	

    

	

    	System

    	Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies

    



  



### **7. Efficiency**



**Communication-Based:**  Improving efficiency by reducing model parameters transmission.



**Hardware-Based:**  Improving efficiency by hardware acceleration (GPU, FPGA, etc.)



**Algorithm-Based:**   Improving efficiency by accelerating model convergence rate (local training, model aggregation, client selection, etc.)



	

	

	

	

	

		Taxonomy

		Papers

		Techniques

		Materials

	

	

		Communication-Based

    	Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients

        Quantization

        

    

	

    	Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning

        Quantization

        

    

	

    	Communication Efficient Federated Learning with Adaptive Quantization

        Quantization

        

    

	

    	QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

        Quantization

        

    

	

    	SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications

        Sparsity

        

    

	

		RPN: A Residual Pooling Network for Efficient Federated Learning

        Sparsity

		

	

	

		FedMD: Heterogenous Federated Learning via Model Distillation

        Knowledge Distillation

		code

	

	

		Ensemble distillation for robust model fusion in federated learning

        Knowledge Distillation

		code

	

	

    	

    	

    	

        

    

	

		Hardware-Based

    	FPGA-Based Hardware Accelerator of Homomorphic Encryption for Efficient Federated Learning

        FPGA-Based Acceleration

        

    

	

    	Hardware Accelerated Learning at the Edge

        GPU-Based Acceleration

        

    

	

    	HAFLO: GPU-Based Acceleration for Federated Logistic Regression

        GPU-Based Acceleration

        

    

	

    	

    	

    	

        

    

	

		Algorithm

    	FetchSGD: Communication-Efficient Federated Learning with Sketching

        Optimization

        Video
Code

    

	

		Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization

        Optimization

        Slide
Video

    

	

    	FedBoost: A Communication-Efficient Algorithm for Federated Learning

        Optimization

        Video

	

	

    	Federated Learning: Strategies for Improving Communication Efficiency

		Optimization

        

	

	

    	One-Shot Federated Learning

		Model Aggregation

        

	



  



### **8. Optimization**



    

	

	

	

	

		Papers

		Application Scenarios

		Conferences/Affiliations

		Materials

	

	

    	Federated Composite Optimization

		loss function contains a non-smooth regularizer

    	ICML 2021(Google)

		code

    

    

    	Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity

		Federated Deep AUC Maximization

        ICML 2021
(The University of Iowa)

        video

	

	

    	Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning

		Non-Convex Objective optimization

        ICML 2021
(The University of Tokyo)

		video

	

	

		From Local SGD to Local Fixed-Point Methods for Federated Learning

		 fixed-point algorithms optimization

        ICML 2020
(Moscow Institute of Physics and Technology; KAUST)

        Slide
Video

	

	

		Federated Learning Based on Dynamic Regularization

		In each round, the objective function for each device dynamically updates its regularizer

        ICLR 2021
(Boston University; ARM)

		

	

	

		Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms

		Formulate federated learning optimization as a posterior inference problem

        ICLR 2021
(CMU; Google)

		code

	

	

		Adaptive Federated Optimization

		Federated versions of adaptive optimizers

        ICLR 2021
(Google)

		code

	

	

		FedBN: Federated Learning on Non-IID Features via Local Batch Normalization 

		How to uses local batch normalization to alleviate the feature shift before averaging models.

        ICLR 2021
(Princeton University)

		code

	

	

		Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning

		SFederated versions of emi-Supervised Learning

        ICLR 2021
(KAIST)

		code

	

	

		Sageflow: Robust Federated Learning against Both Stragglers and Adversaries

		Handle both stragglers (slow devices) and adversaries simultaneously

        NeurIPS 2021
(KAIST)

        HomePage

	

	

		STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning

		Distributed stochastic non-convex optimization

        University of Minnesota

        HomePage

	

	

		Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating

		 Vertical Federated Learning Optimization

         AAAI 2021
(Xidian University; JD Tech)

        video

	



    



### **9. Fairness**



	

	

	

	

	

		Taxonomy

		Papers

		Conferences/Affiliations

		Materials

	

	

		Performance Fairness

    	Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning

        NeurIPS 2021
(Tsinghua University; Alibaba)

        code
HomePage

    

	

    	Fairness-aware Agnostic Federated Learning

        SDM 2021
(University of Arkansas)

        

    

	

    	Fair Resource Allocation in Federated Learning

        ICLR 2020
(CMU; Facebook AI)

        Code

    

	

    	Agnostic Federated Learning

        ICML 2019
(Google)

        

    

	

    	Mitigating Bias in Federated Learning

        arXiv
(IBM)

        

    

	

		Ditto: Fair and Robust Federated Learning Through Personalization

        ICML 2021
(CMU; Facebook AI)

		code
video

	

	

    	

    	

    	

        

    

	

		Client Selection Fairness

    	An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee

        IEEE Transactions on Parallel and Distributed Systems

        

    

	

    	Stochastic Client Selection for Federated Learning with Volatile Clients

        IEEE Internet of Things Journal

        

    

	

    	Federated learning with class imbalance reduction

        European Signal Processing Conference

        

    

	

    	Reputation-Based Federated Learning for Secure Wireless Networks

        ” IEEE Internet of Things Journal

        

    

	

    	

    	

    	

        

    

	

		Contribution Fairness

    	Profit Allocation for Federated Learning

        2019 IEEE International Conference on Big Data

        

    

	

    	Stochastic Client Selection for Federated Learning with Volatile Clients

        IEEE Internet of Things Journal

        

    

	

    	Federated learning with class imbalance reduction

        European Signal Processing Conference

        

    

	

    	Reputation-Based Federated Learning for Secure Wireless Networks

        ” IEEE Internet of Things Journal

        

    



  



### **10. Applications**



    

	

	

	

		Applications

		Papers

		Conferences/Affiliations

		Materials

	

	

    	Computer Vision

    	FedVision: An Online Visual Object Detection Platform Powered by Federated Learning

        WeBank (AAAI 2020)

        code

    

    

    	

    	

    	

        

    

    

    	Nature Language Processing

    	Federated learning for emoji prediction in a mobile keyboard

        Google

        

    

	

    	Federated Learning for Mobile Keyboard Prediction

        Google

        

    

	

    	Applied federated learning: Improving google keyboard query suggestions

        Google

        

    

	

    	Federated Learning Of Out-Of-Vocabulary Words

        Google

        

    

	

    	

    	

    	

        

    

	

    	Automatic Speech Recognition 

    	A Federated Approach in Training Acoustic Models

        MicroSoft (INTERSPEECH 2020)

        Video

    

	

    	Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?

        INRIA (INTERSPEECH 2019)

        

    

	

    	Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework

        Google (ICASSP 2021)

        Google Assistant Help

    

	

    	Federated Evaluation and Tuning for On-Device Personalization: System Design \& Applications

        Apple

        Report

    

	

    	

    	

    	

        

    

	

    	Healthcare

    	Privacy-preserving Federated Brain Tumour Segmentation

        NVIDIA (MICCAI MLMI 2019)

        

    

	

    	Advancing health research with Google Health Studies

        Google

        Blog

    

    

    	Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation

        Intel

        Blog

    

	

    	

    	

    	

        

    

	

    	Blockchain

    	FedCoin: A Peer-to-Peer Payment System for Federated Learning

        Arxiv

        

    

	

    	Blockchained On-Device Federated Learning

        IEEE Communications Letters 2019

        

    



  



### **11. Boosting**



    

	

	

	

		Category

		Papers

		Conferences/Affiliations

		Materials

	

	

    	Tree-Base Boosting

    	Practical Federated Gradient Boosting Decision Trees

        AAAI 2020
(NUS)

        code

    

	

    	Secureboost: A lossless federated learning framework

        IEEE Intelligent Systems 2021
(WeBank; HKUST)

        

    

	

    	Large-scale Secure XGB for Vertical Federated Learning

        CIKM 2021
(Ant Group)

        video

    

    

    	

    	

    	

        

    



  



### **12. Incentive mechanism**



Typically, the incentive mechanism consists of the following two steps:



* How to evaluate the contribution of each participant (Shapley value)



* How to allocate profits based on contributions



    

	

	

	

	

		Steps

		Techniques

		Papers

		Affiliations

		Materials

	

	

    	1. Contribution Evaluation

		Shapley Value

    	Data Shapley: Equitable Valuation of Data for Machine Learning

		ICML 2019
(Stanford University)

		code

    

	

    	A principled approach to data valuation for federated learning

        Arxiv
(Harvard University)

        

    

	

    	Measure contribution of participants in federated learning

        IEEE Big Data
(Swiss Re)

        

    

	

    	GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning

        IAAI 2021
(NTU)

        

    

	

    	Profit allocation for federated learning

        Arxiv
(Beihang University)

        

    

	

    	Fedcoin: A peer-to-peer payment system for federated learning

        NTU

        

    

    

    	

    	

    	

        

    

	

    	2. Profit Allocation

    	Contract Theory

		Hierarchical Incentive Mechanism Design for Federated Machine Learning in Mobile networks

        IEEE Internet of Things Journal 2020

        

    

	

    	Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

        2019 IEEE VTS Asia Pacific Wireless Communications Symposium 

        

    

	

    	Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory

        IEEE Internet of Things Journal 2019

        

    

	

    	A Contract Theory based Incentive Mechanism for Federated Learning

        FTL-IJCAI Workshop 2021

        

    

	

    	Stackelberg Game

		Motivating Workers in Federated Learning: A

Stackelberg Game Perspective

        IEEE Networking Letters, 2020.

        

    

	

    	A Learning-based Incentive

Mechanism for Federated Learning

        IEEE Internet of Things Journal, 2020 

        

    

	

    	Auction

		FMore: An Incentive Scheme of Multi-dimensional

Auction for Federated Learning in MEC

        IEEE ICDCS, 2020.

        

    

	

    	A VCG-based Fair Incentive Mechanism for Federated Learning

        arXiv preprint

        

    

	

    	Toward an Automated Auction Framework for Wireless Federated Learning Services Market

        IEEE Transactions on Mobile Computing, 2020

        

    

	

    	Auction based Incentive Design for Efficient Federated Learning in Cellular Wireless Networks

        2020 IEEE Wireless Communications and Networking Conference

        

    

	

    	FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation

        IEEE INFOCOM, 2021.

        

    

	

    	Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction

        WWW, 2021

        video

    



 



### **13. Unsupervised Learning** 



    

	

	

	

	

		Category

		Papers

		Conferences/Affiliations

		Materials

	

	

    	Clustering

    	Heterogeneity for the Win: One-Shot Federated Clustering

        ICML 2021
(CMU)

		video

    

    

    	

    	

    	

        

    

	

    	Representations Learning

    	Exploiting Shared Representations for Personalized Federated Learning

        ICML 2021
(University of Texas at Austin;
 University of Pennsylvania)

        code
video

    

	

    	Towards Federated Unsupervised Representation Learning

        EdgeSys '20
(Eindhoven University of Technology)

        

    

	

    	Federated Unsupervised Representation Learning

        Zhejiang University

        

    

	

		Collaborative Unsupervised Visual Representation Learning from Decentralized Data

        (ICCV 2021)
(Nanyang Technological University;SenseTime)

		

	

	

		Divergence-aware Federated Self-Supervised Learning

        (ICLR 2022)
(Nanyang Technological University)

		

	

	

		Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering

        (ICML 2022)
(University of Michigan;University of Cambridge)

		code

	



 



### **14. Heterogeneity** 



    

	

	

	

	

		Category

		Papers

		Conferences/Affiliations

		Materials

	

	

    	Data Heterogeneity (NON-IID)

    	Data-Free Knowledge Distillation for Heterogeneous Federated Learning

        ICML 2021
(Michigan State University)

        code
video

    

	

    	Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity

        ICML 2021
(The University of Iowa)

        video

    

	

    	Exploiting Shared Representations for Personalized Federated Learning

        ICML 2021
(University of Texas at Austin;
 University of Pennsylvania)

        code
video

    

	

		SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

        ICML 2020
(EPFL; Google)

        Video

	

	

		Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning

        ICLR 2021
(The Ohio State University)

		

	

	

		HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

        ICLR 2021
(Duke University)

		code

	

	

		FedMix: Approximation of Mixup under Mean Augmented Federated Learning

        ICLR 2021
(KAIST)

		

	

	

		On the Convergence of FedAvg on Non-IID Data

        ICLR 2020
(Peking University)

		Code

	

	

		No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

        NeurIPS 2021
(NUS; Huawei)

		HomePage

	

	

		HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

        AAAI 2022
(The Chinese University of Hong Kong)

		code

	

    

    	

    	

    	

        

    

	

    	Model Heterogeneity

    	Personalized Federated Learning with Moreau Envelopes

        NeurIPS 2020
(The University of Sydney)

        code

    

	

    	Federated Learning of a Mixture of Global and Local Models

        KAUST

        

    

	

		Salvaging Federated Learning by Local Adaptation

        Cornell University

        

	

	

    	

    	

    	

        

    

	

    	Device Heterogeneity

		Sageflow: Robust Federated Learning against Both Stragglers and Adversaries

        NeurIPS 2021
(KAIST)

        HomePage

    

	

		Towards Flexible Device Participation in Federated Learning

        AISTATS 2021
(CMU; Sun Yat-Sen University)

        video
Supplementary

	

	

		Asynchronous Federated Optimization

        12th Annual Workshop on Optimization for Machine Learning

        

	

	

		Pisces: Efficient Federated Learning via Guided Asynchronous Training

        ACM SoCC 2022
(HKUST)

        Code
Slides

	



 



### **15. Client Selection** 



	

	

	

	

		Papers

		Conferences/Affiliations

		Materials

	

	

		Diverse Client Selection for Federated Learning via Submodular Maximization

        ICLR 2022
(Intel; CMU)

		code

	

	

		TiFL: A Tier-based Federated Learning System

        HPDC 2020
(George Mason University)

		

	

	

		HACCS: Heterogeneity-Aware Clustered Client Selection for Accelerated Federated Learning

        IPDPS 2022
(University of Minnesota)

		code

	

	

		Communication-Efficient Federated Learning via Optimal Client Sampling

        University of Texas at Austin

		

	

	

		Oort: Efficient Federated Learning via Guided Participant Selection

        OSDI 2021
(University of Michigan)

		code

	

	

		Optimizing federated learning on non-iid data with reinforcement learning

        IEEE INFOCOM 2020
(University of Toronto)

		

	

	

		Learning Advanced Client Selection Strategy for Federated Learning

        AAAI 2022
(Harvard University)

        

	

	

		Towards understanding biased client selection in federated learning

        AISTATS 2022
(Carnegie Mellon University)

        

	

	

		Stochastic Client Selection for Federated Learning with Volatile Clients

        IEEE Internet of Things Journal 2022
(South China University of Technology)

        

	

	

    	Oort: Efficient Federated Learning via Guided Participant Selection

        OSDI 2021
(University of Michigan)

		code
video

    

	

    	Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning

        ICML 2021
(Accenture)

		code
video

    

	

		Federated Multi-Armed Bandits

        AAAI 2021
(University of Virginia)

        code
video

	

	

		FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

        AISTATS 2020
(UC Santa Barbara; UT Austin)

        video
Supplementary

	



 



### **16. Graph Neural Networks** 



	

	

	

	

	

		Category

		Papers

		Conferences/Affiliations

		Materials

	

	

		Graph-Level

    	Federated Graph Classification over Non-IID Graphs

        NeurIPS 2021
(Emory University)

		

    

	

    	Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting

        arXiv
(University of Rochester)

		

    

	

    	FedGCN: Federated Learning-Based Graph Convolutional Networks for Non-Euclidean Spatial Data

        Mathematics 2022
(Nanjing University of Information Science and Technology)

		

    

	

    	

    	

    	

        

    

	

		SubGraph-Level

    	FedGraph: Federated Graph Learning with Intelligent Sampling

         IEEE TPDS
(University of Aizu)

		

    

	

    	Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling

        IWQOS 2021
(University of Aizu)

		

    

	

    	PPSGCN: A Privacy-Preserving Subgraph Sampling Based Distributed GCN Training Method

        Arxiv
(Xi’an Jiaotong University)

		

    

	

    	Subgraph Federated Learning with Missing Neighbor Generation

        NeurIPS 2021
(Emory University;)

		HomePage

    

	

    	FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction

        Arxiv
( University of Electronic Science and Technology of China)

		

    

	

    	FedGL: Federated Graph Learning Framework with Global Self-Supervision

        Arxiv
(Sun Yat-sen University)

		

    

	

		

    	Personalized Subgraph Federated Learning

        ICML 2023
(KAIST)

		

    

	

    	

    	

    	

        

    

	

		Node-Level

    	Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling

        KDD 2021
(University of Southern California)

		

    

	

    	Personalized Federated Learning With Graph

        IJCAI 2022
(Beihang University)

		code

    

	

    	BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning

        FL-ICML Workshop 2021
(Nanyang Technological University)

		

    

	

    	Decentralized Federated Graph Neural Networks

        FL-IJCAI Workshop 2021
(Blue Elephant Tec)

		

    

	

    	Peer-to-Peer Federated Learning on Graphs

        Arxiv
(University of California)

		

    

	

    	A Graph Federated Architecture with Privacy

Preserving Learning

        Arxiv
(EPFL)

		

    

	

    	Decentralized Federated Learning for Electronic Health Records

        Arxiv
(IBM)

		

    

	

    	Learn Electronic Health Records by Fully Decentralized Federated Learning

        FL-NeurIPS 2019
(University of Minnesota)

		

    

	

    	Decentralized Federated Learning via SGD over

Wireless D2D Networks

        Arxiv
(Shenzhen University)

		

    

	

    	SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data

        AAAI 2022
(university of Southern California)

		code

    

	

    	A New Look and Convergence Rate of Federated

Multi-Task Learning with Laplacian Regularization

        Arxiv
(The University of Sydney)

		code

    

	

    	Decentralized Event-Triggered Federated Learning

with Heterogeneous Communication Thresholds

        Arxiv
( Purdue University)

		code

    



  



### **18. Other Machine Learning Paradigm** 



    

	

	

	

		Taxonomy

		Papers

		Conferences/Affiliations

		Materials

	

	

    	Neural Architecture Search

    	Federated Neural Architecture Search

        Beijing University of Posts and Telecommunications

		

    

	

		FedNAS: Federated Deep Learning via Neural Architecture Search

        CVPR 2020 workshop
(University of Southern California)

        

	

	

		Self-supervised cross-silo federated neural architecture search

        WeBank

        

	

    

    	

    	

    	

        

    

    

    	Active Learning

    	Active Federated Learning

        University of Michigan

        

    

	

        Active learning based federated learning for waste and natural disaster image classification

         Hamad Bin Khalifa University 

		

	

    

    	Federated Active Learning

        Harvard Medical School

        

    

    

    	

    	

    	

        

    

    

    	Continual Learning

    	Federated Continual Learning with Weighted Inter-client Transfer

        KAIST (ICML 2021)

        code

    

    

    	Partitioned variational inference: A unified framework encompassing federated and continual learning

        University of Sydney

        

    

    

    	A distillation-based approach integrating continual learning and federated learning for pervasive services

        Inria

        

    

	

    	FedSpeech: Federated Text-to-Speech with Continual Learning

        Zhejiang University

        

    



 



### **19. Computational Learning Theory**



Privacy, utility, and efficiency are the three key concepts of trustworthy federated learning. We point out that there is no security mechanism that can achieve optimality in terms of privacy leakage, utility loss, and efficiency loss simultaneously.



 * [No Free Lunch Theorem for Security and Utility in Federated Learning](https://arxiv.org/pdf/2203.05816.pdf)

 * [Trading Off Privacy, Utility and Efficiency in Federated Learning](https://arxiv.org/pdf/2209.00230.pdf) 

 * [Probably Approximately Correct Federated Learning](https://arxiv.org/pdf/2304.04641.pdf) 

 * [A Game-theoretic Framework for Federated Learning](https://arxiv.org/pdf/2304.05836.pdf)  

 * [Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning](https://arxiv.org/pdf/2305.00312.pdf)  



 



## Google FL Workshops

 * [Google Workshop on Federated Learning and Analytics 2022](https://www.youtube.com/watch?v=ZokMxYDlCJk&list=PLSIUOFhnxEiA-Ky_BuirTS6Amb9A60uSH&ab_channel=GoogleTechTalks)

 * [Google Workshop on Federated Learning and Analytics 2021](https://www.youtube.com/playlist?list=PLSIUOFhnxEiD9uihG5t9ABdPhSVqQ3HWA)

 * [Google Workshop on Federated Learning and Analytics 2020](https://www.youtube.com/playlist?list=PLSIUOFhnxEiCJS8q6SYdc0944xlV_6Jbu) 

 * [Google Federated Learning workshop 2019](https://sites.google.com/view/federated-learning-2019/home) 



  



## Videos and Lectures



 * [TensorFlow Federated Tutorial Session](https://www.youtube.com/watch?v=JBNas6Yd30A&ab_channel=GoogleTechTalks) 

  

 * [TensorFlow Federated (TFF): Machine Learning on Decentralized Data ](https://www.youtube.com/watch?v=1YbPmkChcbo) - Google, TF Dev Summit ‘19 2019



 * [Federated Learning: Machine Learning on Decentralized Data](https://www.youtube.com/watch?v=89BGjQYA0uE) - Google, Google I/O 2019



 * [Federated Learning](https://www.youtube.com/watch?v=xJkY3ehX_MI) - Cloudera Fast Forward Labs, DataWorks Summit 2019



 * [GDPR, Data Shortage and AI](https://vimeo.com/313941621) - Qiang Yang, AAAI 2019 Invited Talk



 * [Code Tutorial: From Centralized to Federated](https://www.youtube.com/watch?v=Ky6TicaPfVI) - Flower Summit 2021



   



## Tutorials and Blogs



 * [What is Federated Learning](https://blogs.nvidia.com/blog/2019/10/13/what-is-federated-learning/) - Nvidia 2019

 * [Online Federated Learning Comic](https://federated.withgoogle.com/) - Google 2019

 * [Federated Learning: Collaborative Machine Learning without Centralized Training Data](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html) - Google AI Blog 2017

 * [Go Federated with OpenFL](https://medium.com/@igor.davidyuk_98364/go-federated-with-openfl-8bc145a5ead1) - Intel 2021



  



## Open-Sources



Developing a federated learning framework from scratch is very time-consuming, especially in industrial. An excellent FL framework can facilitate engineers and researchers to train, research and deploy the FL model in practice. In this section, we summarize some commonly used open-source FL frameworks from both industrial and academia perspectives. 



### Enterprise Grade



    

	

	

	

		Platform

		Papers

		Affiliations/HomePage

	

	

    	FATE

        FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection

		WeBank

	

	

    	FedML

    	FedML: A Research Library and Benchmark for Federated Machine Learning

		fedml.ai

	

	

    	OpenFL

    	OpenFL: An open-source framework for Federated Learning

		Intel

	

	

    	NVFlare

    	

		NVIDIA

	

	

    	IBM Federated Learning

    	IBM Federated Learning: an Enterprise Framework White Paper

		IBM

	

	

    	Fedlearner

    	

		Bytedance

	

	

    	PaddleFL

    	

		Baidu

	

	

    	Sherpa.ai Federated Learning 

    	Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy

		Sherpa.ai

			

	

    	FederatedScope

    	FederatedScope: A Flexible Federated Learning Platform for Heterogeneity

		Alibaba

			

	

    	secretflow

    	

		Antgroup

		

	

    	FEDn

        Scalable federated machine learning with FEDn

		Scaleout Systems

	



  



### Research Purpose 



    

	

	

	

		Platform

		Papers

		Affiliations/HomePage

	

	

    	Tensorflow-Federated

    	Towards Federated Learning at Scale: System Design

		Google

    

	

    	FedJAX

    	FEDJAX: Federated learning simulation with JAX

		Google

    

	

    	Flower

    	Flower: A Friendly Federated Learning Research Framework

		flower.dev

	

	

    	FLUTE

    	FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations

		Microsoft

    

	

    	FLSim

    	

		Meta

    

    

    	PySyft

    	A generic framework for privacy preserving deep learning

		OpenMined

	

	

    	PyVertical

    	PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN

		OpenMined

	

	

    	LEAF

    	LEAF: A Benchmark for Federated Settings

		CMU

	

	

    	FedScale

    	FedScale: Benchmarking Model and System Performance of Federated Learning at Scale

		SymbioticLab(University of Michigan)

	

	

    	EasyFL

    	EasyFL: A Low-code Federated Learning Platform For Dummies

		NTU

		

	

    	FedLab

    	FedLab: A Flexible Federated Learning Framework

		SMILELab-FL

		

	

    	Galaxy Federated Learning

    	GFL: A Decentralized Federated Learning Framework Based On Blockchain

		Zhejiang University

		

	

    	FedTree

    	

		National University of Singapore

		



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