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

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

• Host: GitHub
• URL: https://github.com/innovation-cat/Awesome-Federated-Machine-Learning
• Owner: innovation-cat
• Created: 2019-10-19T10:35:34.000Z (over 6 years ago)
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  • Readme: README.md

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• awesome-awesome-artificial-intelligence - Awesome Federated Machine Learning - cat/Awesome-Federated-Machine-Learning?style=social) | (Privacy & Safety)
• awesome-artificial-intelligence-research - Awesome Federated Machine Learning - federated ML papers, books, code, tutorials, and videos. (Core Machine Learning Research / Federated and Privacy-Preserving ML)

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.  







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