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Awesome-Federated-Learning
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
https://github.com/chaoyanghe/Awesome-Federated-Learning
Last synced: 4 days ago
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Federated Learning Library - FedML https://fedml.ai
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Publications in Top-tier ML/CV/NLP/DM Conference (ICML, NeurIPS, ICLR, CVPR, ACL, AAAI, KDD)
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ICML
- SCAFFOLD: Stochastic Controlled Averaging for Federated Learning - I.I.D) | nonconvex/convex optimization with variance reduction |
- FedBoost: A Communication-Efficient Algorithm for Federated Learning
- FetchSGD: Communication-Efficient Federated Learning with Sketching
- Federated Learning with Only Positive Labels - class classification | regularization |
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Distributed optimization
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NeurIPS
- Communication-Efficient Learning of Deep Networks from Decentralized Data. 2016-02. AISTAT 2017.
- Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. NeurIPS 2020
- Federated Optimization: Distributed Optimization Beyond the Datacenter. NIPS 2016 workshop.
- Federated Optimization: Distributed Machine Learning for On-Device Intelligence
- Stochastic, Distributed and Federated Optimization for Machine Learning. FL PhD Thesis. By Jakub
- Collaborative Deep Learning in Fixed Topology Networks
- Federated Multi-Task Learning
- LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
- Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms
- Exact Support Recovery in Federated Regression with One-shot Communication
- DEED: A General Quantization Scheme for Communication Efficiency in Bits
- A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization
- FedSplit: An algorithmic framework for fast federated optimization
- Distributed Stochastic Non-Convex Optimization: Momentum-Based Variance Reduction
- Federated Residual Learning. 2020-03
- Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. ICML 2020.
- LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning
- Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor
- Dynamic Federated Learning
- Distributed Optimization over Block-Cyclic Data
- Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
- Federated Learning with Matched Averaging
- Federated Learning of a Mixture of Global and Local Models
- Faster On-Device Training Using New Federated Momentum Algorithm
- FedDANE: A Federated Newton-Type Method
- Distributed Fixed Point Methods with Compressed Iterates
- Primal-dual methods for large-scale and distributed convex optimization and data analytics
- Parallel Restarted SPIDER - Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity
- Representation of Federated Learning via Worst-Case Robust Optimization Theory
- On the Convergence of Local Descent Methods in Federated Learning
- SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
- Accelerating Federated Learning via Momentum Gradient Descent
- Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction
- Gradient Descent with Compressed Iterates
- First Analysis of Local GD on Heterogeneous Data
- (*) On the Convergence of FedAvg on Non-IID Data. ICLR 2020.
- Robust Federated Learning in a Heterogeneous Environment
- Scalable and Differentially Private Distributed Aggregation in the Shuffled Model
- Variational Federated Multi-Task Learning
- Bayesian Nonparametric Federated Learning of Neural Networks. ICLR 2019.
- Differentially Private Learning with Adaptive Clipping
- Semi-Cyclic Stochastic Gradient Descent
- Asynchronous Federated Optimization
- Agnostic Federated Learning
- Federated Optimization in Heterogeneous Networks
- Learning Rate Adaptation for Federated and Differentially Private Learning
- Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets
- Adaptive Federated Learning in Resource Constrained Edge Computing Systems
- Adaptive Federated Optimization
- Local SGD converges fast and communicates little
- Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD
- Local SGD With a Communication Overhead Depending Only on the Number of Workers
- Tighter Theory for Local SGD on Identical and Heterogeneous Data
- STL-SGD: Speeding Up Local SGD with Stagewise Communication Period
- Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms
- Understanding Unintended Memorization in Federated Learning
- Communication-Efficient Learning of Deep Networks from Decentralized Data. 2016-02. AISTAT 2017.
- Collaborative Deep Learning in Fixed Topology Networks
- Federated Multi-Task Learning
- Primal-dual methods for large-scale and distributed convex optimization and data analytics
- Variational Federated Multi-Task Learning
- Adaptive Federated Learning in Resource Constrained Edge Computing Systems
- Local SGD converges fast and communicates little
- Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD
- Local SGD With a Communication Overhead Depending Only on the Number of Workers
- Federated Accelerated Stochastic Gradient Descent
- Tighter Theory for Local SGD on Identical and Heterogeneous Data
- STL-SGD: Speeding Up Local SGD with Stagewise Communication Period
- Understanding Unintended Memorization in Federated Learning
- Robust Federated Learning: The Case of Affine Distribution Shifts
- On the Outsized Importance of Learning Rates in Local Update Methods
- Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. ICML 2020.
- Don't Use Large Mini-Batches, Use Local SGD
- Federated Accelerated Stochastic Gradient Descent
- From Local SGD to Local Fixed-Point Methods for Federated Learning
- Adaptive Federated Optimization. ICLR 2021 (Under Review). 2020-02-29
- Federated Optimization: Distributed Optimization Beyond the Datacenter. NIPS 2016 workshop.
- Federated Optimization: Distributed Machine Learning for On-Device Intelligence
- Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning
- Exact Support Recovery in Federated Regression with One-shot Communication
- DEED: A General Quantization Scheme for Communication Efficiency in Bits
- Personalized Federated Learning with Moreau Envelopes
- Towards Flexible Device Participation in Federated Learning for Non-IID Data
- A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization
- FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data
- FedSplit: An algorithmic framework for fast federated optimization
- Distributed Stochastic Non-Convex Optimization: Momentum-Based Variance Reduction
- Federated Learning with Compression: Unified Analysis and Sharp Guarantees
- Federated Residual Learning. 2020-03
- LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning
- Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor
- Dynamic Federated Learning
- Distributed Optimization over Block-Cyclic Data
- Federated Learning with Matched Averaging
- Faster On-Device Training Using New Federated Momentum Algorithm
- FedDANE: A Federated Newton-Type Method
- Distributed Fixed Point Methods with Compressed Iterates
- Parallel Restarted SPIDER - Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity
- On the Convergence of Local Descent Methods in Federated Learning
- SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
- Central Server Free Federated Learning over Single-sided Trust Social Networks
- Representation of Federated Learning via Worst-Case Robust Optimization Theory
- Accelerating Federated Learning via Momentum Gradient Descent
- Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction
- Gradient Descent with Compressed Iterates
- First Analysis of Local GD on Heterogeneous Data
- (*) On the Convergence of FedAvg on Non-IID Data. ICLR 2020.
- Robust Federated Learning in a Heterogeneous Environment
- Scalable and Differentially Private Distributed Aggregation in the Shuffled Model
- Bayesian Nonparametric Federated Learning of Neural Networks. ICLR 2019.
- Differentially Private Learning with Adaptive Clipping
- Semi-Cyclic Stochastic Gradient Descent
- Asynchronous Federated Optimization
- Agnostic Federated Learning
- Federated Optimization in Heterogeneous Networks
- Learning Rate Adaptation for Federated and Differentially Private Learning
- Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets
- From Local SGD to Local Fixed-Point Methods for Federated Learning
- Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms
- Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
- Federated Learning of a Mixture of Global and Local Models
- Partitioned Variational Inference: A unified framework encompassing federated and continual learning
- An Efficient Framework for Clustered Federated Learning
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Non-IID and Model Personalization
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NeurIPS
- The Non-IID Data Quagmire of Decentralized Machine Learning. 2019-10
- Federated Learning with Non-IID Data
- FedCD: Improving Performance in non-IID Federated Learning. 2020
- Life Long Learning: FedFMC: Sequential Efficient Federated Learning on Non-iid Data. 2020
- Personalized Federated Learning using Hypernetworks. 2021
- Ensemble Distillation for Robust Model Fusion in Federated Learning. 2020
- XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning. 2020
- Continual Local Training for Better Initialization of Federated Models. 2020
- Global Multiclass Classification from Heterogeneous Local Models. 2020
- Multi-Center Federated Learning. 2020
- (*) FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning. CMU ECE. 2020-04-07
- (*) Adaptive Personalized Federated Learning
- Semi-Federated Learning
- Device Heterogeneity in Federated Learning: A Superquantile Approach. 2020-02
- Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
- Three Approaches for Personalization with Applications to Federated Learning
- Personalized Federated Learning: A Meta-Learning Approach
- Towards Federated Learning: Robustness Analytics to Data Heterogeneity
- Salvaging Federated Learning by Local Adaptation
- FOCUS: Dealing with Label Quality Disparity in Federated Learning. 2020-01
- Overcoming Noisy and Irrelevant Data in Federated Learning. ICPR 2020.
- Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning. 2020-01
- (*) Think Locally, Act Globally: Federated Learning with Local and Global Representations. NeurIPS 2019 Workshop on Federated Learning distinguished student paper award
- Federated Learning with Personalization Layers
- Federated Evaluation of On-device Personalization
- Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating
- Overcoming Forgetting in Federated Learning on Non-IID Data
- Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
- Improving Federated Learning Personalization via Model Agnostic Meta Learning
- Measure Contribution of Participants in Federated Learning
- (*) Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
- Multi-hop Federated Private Data Augmentation with Sample Compression
- Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications
- Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
- Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data
- High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
- Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- Federated Meta-Learning with Fast Convergence and Efficient Communication
- Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters
- Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity
- Client Adaptation improves Federated Learning with Simulated Non-IID Clients
- Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
- Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity. ICDCS 2021.
- The Non-IID Data Quagmire of Decentralized Machine Learning. 2019-10
- Federated Learning with Non-IID Data
- FedCD: Improving Performance in non-IID Federated Learning. 2020
- Life Long Learning: FedFMC: Sequential Efficient Federated Learning on Non-iid Data. 2020
- Ensemble Distillation for Robust Model Fusion in Federated Learning. 2020
- XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning. 2020
- Continual Local Training for Better Initialization of Federated Models. 2020
- Global Multiclass Classification from Heterogeneous Local Models. 2020
- Multi-Center Federated Learning. 2020
- (*) FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning. CMU ECE. 2020-04-07
- (*) Adaptive Personalized Federated Learning
- Semi-Federated Learning
- Device Heterogeneity in Federated Learning: A Superquantile Approach. 2020-02
- Personalized Federated Learning: A Meta-Learning Approach
- Towards Federated Learning: Robustness Analytics to Data Heterogeneity
- Overcoming Noisy and Irrelevant Data in Federated Learning. ICPR 2020.
- Salvaging Federated Learning by Local Adaptation
- Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning. 2020-01
- (*) Think Locally, Act Globally: Federated Learning with Local and Global Representations. NeurIPS 2019 Workshop on Federated Learning distinguished student paper award
- FOCUS: Dealing with Label Quality Disparity in Federated Learning. 2020-01
- Federated Learning with Personalization Layers
- Federated Evaluation of On-device Personalization
- Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating
- Overcoming Forgetting in Federated Learning on Non-IID Data
- Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
- Improving Federated Learning Personalization via Model Agnostic Meta Learning
- (*) Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
- Multi-hop Federated Private Data Augmentation with Sample Compression
- Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications
- Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
- Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data
- High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
- Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters
- Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity
- Client Adaptation improves Federated Learning with Simulated Non-IID Clients
- Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
- NeurIPS 2020 submission: An Efficient Framework for Clustered Federated Learning. 2020
- FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data. 2020
- Robust Federated Learning: The Case of Affine Distribution Shifts. 2020
- Towards Flexible Device Participation in Federated Learning for Non-IID Data. 2020
- Federated Learning with Only Positive Labels. 2020
- Robust and Communication-Efficient Federated Learning from Non-IID Data
- Robust and Communication-Efficient Federated Learning from Non-IID Data
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Vertical Federated Learning
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NeurIPS
- SecureBoost: A Lossless Federated Learning Framework
- Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
- A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
- Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
- Entity Resolution and Federated Learning get a Federated Resolution.
- Multi-Participant Multi-Class Vertical Federated Learning
- A Communication-Efficient Collaborative Learning Framework for Distributed Features
- Asymmetrical Vertical Federated Learning
- VAFL: a Method of Vertical Asynchronous Federated Learning, ICML workshop on FL, 2020
- SecureBoost: A Lossless Federated Learning Framework
- Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
- A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
- Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
- Entity Resolution and Federated Learning get a Federated Resolution.
- Multi-Participant Multi-Class Vertical Federated Learning
- A Communication-Efficient Collaborative Learning Framework for Distributed Features
- Asymmetrical Vertical Federated Learning
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Decentralized FL
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NeurIPS
- Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
- Multi-consensus Decentralized Accelerated Gradient Descent
- Decentralized Bayesian Learning over Graphs. 2019-05
- BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
- Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
- Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
- Multi-consensus Decentralized Accelerated Gradient Descent
- Decentralized Bayesian Learning over Graphs. 2019-05
- BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
- Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
- Central Server Free Federated Learning over Single-sided Trust Social Networks
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Hierarchical FL
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NeurIPS
- Client-Edge-Cloud Hierarchical Federated Learning
- Client-Edge-Cloud Hierarchical Federated Learning
- Matcha: Speeding Up Decentralized SGD via Matching Decomposition Sampling
- Federated learning with hierarchical clustering of local updates to improve training on non-IID data. 2020
- Matcha: Speeding Up Decentralized SGD via Matching Decomposition Sampling
- Federated learning with hierarchical clustering of local updates to improve training on non-IID data. 2020
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Neural Architecture Search
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NeurIPS
- FedNAS: Federated Deep Learning via Neural Architecture Search. CVPR 2020. 2020-04-18
- Real-time Federated Evolutionary Neural Architecture Search. 2020-03
- Federated Neural Architecture Search. 2020-06-14
- Differentially-private Federated Neural Architecture Search. 2020-06
- FedNAS: Federated Deep Learning via Neural Architecture Search. CVPR 2020. 2020-04-18
- FedNAS: Federated Deep Learning via Neural Architecture Search. CVPR 2020. 2020-04-18
- Real-time Federated Evolutionary Neural Architecture Search. 2020-03
- Federated Neural Architecture Search. 2020-06-14
- Differentially-private Federated Neural Architecture Search. 2020-06
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Transfer Learning
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NeurIPS
- Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
- Secure Federated Transfer Learning. IEEE Intelligent Systems 2018.
- FedMD: Heterogenous Federated Learning via Model Distillation
- Secure and Efficient Federated Transfer Learning
- Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data
- Federated Reinforcement Distillation with Proxy Experience Memory
- Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
- Secure Federated Transfer Learning. IEEE Intelligent Systems 2018.
- FedMD: Heterogenous Federated Learning via Model Distillation
- Secure and Efficient Federated Transfer Learning
- Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data
- Federated Reinforcement Distillation with Proxy Experience Memory
- Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning. 2020
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Continual Learning
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Semi-Supervised Learning
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NeurIPS
- Semi-supervised knowledge transfer for deep learning from private training data. ICLR 2017
- Scalable private learning with PATE. ICLR 2018.
- Semi-supervised knowledge transfer for deep learning from private training data. ICLR 2017
- Scalable private learning with PATE. ICLR 2018.
- Federated Semi-Supervised Learning with Inter-Client Consistency. 2020
- Federated Semi-Supervised Learning with Inter-Client Consistency. 2020
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Reinforcement Learning
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Bayesian Learning
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Causal Learning
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Adversarial Attack and Defense
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NeurIPS
- An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies. 2020-04-01
- How To Backdoor Federated Learning. 2018-07-02. AISTATS 2020
- Can You Really Backdoor Federated Learning?. NeruIPS 2019. 2019-11-18
- DBA: Distributed Backdoor Attacks against Federated Learning. ICLR 2020.
- CRFL: Certifiably Robust Federated Learning against Backdoor Attacks. ICML 2021.
- Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. ACM CCS 2017. 2017-02-14
- Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
- Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. 2018-12-03
- Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning. INFOCOM 2019
- Analyzing Federated Learning through an Adversarial Lens. ICML 2019.
- Mitigating Sybils in Federated Learning Poisoning. 2018-08-14. RAID 2020
- RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets, AAAI 2019
- (*) A Framework for Evaluating Gradient Leakage Attacks in Federated Learning. 2020-04-22
- (*) Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. 2019-11-26
- NeurIPS 2020 Submission: Backdoor Attacks on Federated Meta-Learning
- Towards Realistic Byzantine-Robust Federated Learning. 2020-04-10
- Data Poisoning Attacks on Federated Machine Learning. 2020-04-19
- Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning. 2020-04-27
- Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. 2020-06-22
- (*) NeurIPS 2020 submission: FedMGDA+: Federated Learning meets Multi-objective Optimization. 2020-06-20
- (*) NeurIPS 2020 submission: Free-rider Attacks on Model Aggregation in Federated Learning. 2020-06-26
- FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications. 2020-06-28
- Privacy-preserving Weighted Federated Learning within Oracle-Aided MPC Framework. 2020-05-17
- BASGD: Buffered Asynchronous SGD for Byzantine Learning. 2020-03-02
- Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees. 2020-02-25
- Learning to Detect Malicious Clients for Robust Federated Learning. 2020-02-01
- Robust Aggregation for Federated Learning. 2019-12-31
- Towards Deep Federated Defenses Against Malware in Cloud Ecosystems. 2019-12-27
- Attack-Resistant Federated Learning with Residual-based Reweighting. 2019-12-23
- Free-riders in Federated Learning: Attacks and Defenses. 2019-11-28
- Robust Federated Learning with Noisy Communication. 2019-11-01
- Abnormal Client Behavior Detection in Federated Learning. 2019-10-22
- Eavesdrop the Composition Proportion of Training Labels in Federated Learning. 2019-10-14
- Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging. 2019-09-11
- An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning. 2019-08-22
- Secure Distributed On-Device Learning Networks With Byzantine Adversaries. 2019-06-03
- Robust Federated Training via Collaborative Machine Teaching using Trusted Instances. 2019-05-03
- Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting. 2018-11-23
- Inverting Gradients - How easy is it to break privacy in federated learning? 2020-03-31
- Quantification of the Leakage in Federated Learning. 2019-10-12
- An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies. 2020-04-01
- How To Backdoor Federated Learning. 2018-07-02. AISTATS 2020
- Can You Really Backdoor Federated Learning?. NeruIPS 2019. 2019-11-18
- Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. ACM CCS 2017. 2017-02-14
- Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
- Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. 2018-12-03
- Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning. INFOCOM 2019
- Analyzing Federated Learning through an Adversarial Lens. ICML 2019.
- Mitigating Sybils in Federated Learning Poisoning. 2018-08-14. RAID 2020
- (*) A Framework for Evaluating Gradient Leakage Attacks in Federated Learning. 2020-04-22
- (*) Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. 2019-11-26
- NeurIPS 2020 Submission: Backdoor Attacks on Federated Meta-Learning
- Towards Realistic Byzantine-Robust Federated Learning. 2020-04-10
- Data Poisoning Attacks on Federated Machine Learning. 2020-04-19
- Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning. 2020-04-27
- Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. 2020-06-22
- (*) NeurIPS 2020 submission: FedMGDA+: Federated Learning meets Multi-objective Optimization. 2020-06-20
- FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications. 2020-06-28
- Privacy-preserving Weighted Federated Learning within Oracle-Aided MPC Framework. 2020-05-17
- BASGD: Buffered Asynchronous SGD for Byzantine Learning. 2020-03-02
- Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees. 2020-02-25
- Learning to Detect Malicious Clients for Robust Federated Learning. 2020-02-01
- Robust Aggregation for Federated Learning. 2019-12-31
- Towards Deep Federated Defenses Against Malware in Cloud Ecosystems. 2019-12-27
- Attack-Resistant Federated Learning with Residual-based Reweighting. 2019-12-23
- Free-riders in Federated Learning: Attacks and Defenses. 2019-11-28
- Robust Federated Learning with Noisy Communication. 2019-11-01
- Abnormal Client Behavior Detection in Federated Learning. 2019-10-22
- Eavesdrop the Composition Proportion of Training Labels in Federated Learning. 2019-10-14
- Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging. 2019-09-11
- An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning. 2019-08-22
- Secure Distributed On-Device Learning Networks With Byzantine Adversaries. 2019-06-03
- Robust Federated Training via Collaborative Machine Teaching using Trusted Instances. 2019-05-03
- Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting. 2018-11-23
- Inverting Gradients - How easy is it to break privacy in federated learning? 2020-03-31
- Quantification of the Leakage in Federated Learning. 2019-10-12
- Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer. 2019-12-24
- Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer. 2019-12-24
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Privacy
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NeurIPS
- Practical Secure Aggregation for Federated Learning on User-Held Data. NIPS 2016 workshop
- Differentially Private Federated Learning: A Client Level Perspective. NIPS 2017 Workshop
- Exploiting Unintended Feature Leakage in Collaborative Learning. S&P 2019. 2018-05-10
- (x) Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning. 2018-05
- A Hybrid Approach to Privacy-Preserving Federated Learning. AISec 2019. 2018-12-07
- A generic framework for privacy preserving deep learning. PPML 2018. 2018-11-09
- Federated Generative Privacy. IJCAI 2019 FL workshop. 2019-10-08
- Enhancing the Privacy of Federated Learning with Sketching. 2019-11-05
- Federated Learning with Bayesian Differential Privacy. 2019-11-22
- https://aisec.cc/
- Private Federated Learning with Domain Adaptation. NeurIPS 2019 FL workshop. 2019-12-13
- iDLG: Improved Deep Leakage from Gradients. 2020-01-08
- Practical and Bilateral Privacy-preserving Federated Learning. 2020-02-23
- Decentralized Policy-Based Private Analytics. 2020-03-14
- FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection. DASFAA 2020. 2020-03-24
- Learn to Forget: User-Level Memorization Elimination in Federated Learning. 2020-03-24
- LDP-Fed: Federated Learning with Local Differential Privacy. EdgeSys 2020. 2020-04-01
- PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks. 2020-04-05
- Local Differential Privacy based Federated Learning for Internet of Things. 2020-04-09
- Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise. 2020-04.
- Privacy Preserving Distributed Machine Learning with Federated Learning. 2020-04-25
- Exploring Private Federated Learning with Laplacian Smoothing. 2020-05-01
- Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning. 2020-05-05
- Efficient Privacy Preserving Edge Computing Framework for Image Classification. 2020-05-10
- A Distributed Trust Framework for Privacy-Preserving Machine Learning. 2020-06-03
- Secure Byzantine-Robust Machine Learning. 2020-06-08
- ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing. 2020-06-08
- Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control. 2020-06-09
- (*) Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties. 2020-06-12
- Federated Learning with Differential Privacy:Algorithms and Performance Analysis
- Practical Secure Aggregation for Federated Learning on User-Held Data. NIPS 2016 workshop
- Differentially Private Federated Learning: A Client Level Perspective. NIPS 2017 Workshop
- A Hybrid Approach to Privacy-Preserving Federated Learning. AISec 2019. 2018-12-07
- A generic framework for privacy preserving deep learning. PPML 2018. 2018-11-09
- Federated Generative Privacy. IJCAI 2019 FL workshop. 2019-10-08
- Enhancing the Privacy of Federated Learning with Sketching. 2019-11-05
- Federated Learning with Bayesian Differential Privacy. 2019-11-22
- https://aisec.cc/
- Private Federated Learning with Domain Adaptation. NeurIPS 2019 FL workshop. 2019-12-13
- iDLG: Improved Deep Leakage from Gradients. 2020-01-08
- Practical and Bilateral Privacy-preserving Federated Learning. 2020-02-23
- Decentralized Policy-Based Private Analytics. 2020-03-14
- LDP-Fed: Federated Learning with Local Differential Privacy. EdgeSys 2020. 2020-04-01
- FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection. DASFAA 2020. 2020-03-24
- Learn to Forget: User-Level Memorization Elimination in Federated Learning. 2020-03-24
- PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks. 2020-04-05
- Local Differential Privacy based Federated Learning for Internet of Things. 2020-04-09
- Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise. 2020-04.
- Privacy Preserving Distributed Machine Learning with Federated Learning. 2020-04-25
- Exploring Private Federated Learning with Laplacian Smoothing. 2020-05-01
- Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning. 2020-05-05
- Efficient Privacy Preserving Edge Computing Framework for Image Classification. 2020-05-10
- A Distributed Trust Framework for Privacy-Preserving Machine Learning. 2020-06-03
- Secure Byzantine-Robust Machine Learning. 2020-06-08
- ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing. 2020-06-08
- Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control. 2020-06-09
- (*) Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties. 2020-06-12
- Federated Learning with Differential Privacy:Algorithms and Performance Analysis
- Decentralized Differentially Private Segmentation with PATE. MICCAI 2020 Under Review. 2020-04
- Enhancing Privacy via Hierarchical Federated Learning. 2020-04-23
- GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators. 2020-06-15
- Enhancing Privacy via Hierarchical Federated Learning. 2020-04-23
- Decentralized Differentially Private Segmentation with PATE. MICCAI 2020 Under Review. 2020-04
-
-
Fairness
-
NeurIPS
- Fair Resource Allocation in Federated Learning. ICLR 2020.
- Hierarchically Fair Federated Learning
- Towards Fair and Privacy-Preserving Federated Deep Models
- Fair Resource Allocation in Federated Learning. ICLR 2020.
- Hierarchically Fair Federated Learning
- Towards Fair and Privacy-Preserving Federated Deep Models
-
-
Incentive Mechanism
-
NeurIPS
- Toward an Automated Auction Framework for Wireless Federated Learning Services Market
- Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
- Motivating Workers in Federated Learning: A Stackelberg Game Perspective
- Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
- A Learning-based Incentive Mechanism forFederated Learning
- A Crowdsourcing Framework for On-Device Federated Learning
- Toward an Automated Auction Framework for Wireless Federated Learning Services Market
- Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
- Motivating Workers in Federated Learning: A Stackelberg Game Perspective
- Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
-
-
Communication Efficiency
-
NeurIPS
- Federated Learning: Strategies for Improving Communication Efficiency
- Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training. ICLR 2018. 2017-12-05
- NeurIPS 2020 submission: Artemis: tight convergence guarantees for bidirectional compression in Federated Learning. 2020-06-25
- Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC. 2020-06-21
- (x) Federated Mutual Learning. 2020-06-27
- A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. 2020-06-19
- Federated Learning With Quantized Global Model Updates. 2020-06-18
- Evaluating the Communication Efficiency in Federated Learning Algorithm. 2020-04-06
- Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning. 2020-05-21
- Ternary Compression for Communication-Efficient Federated Learning. 2020-05-07
- Gradient Statistics Aware Power Control for Over-the-Air Federated Learning. 2020-05-04
- Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection. 2020-02-22
- (*) RPN: A Residual Pooling Network for Efficient Federated Learning. ECAI 2020.
- Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning. 2020-01-22
- Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning. 2019-11-12
- L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning
- Gradient Sparification for Asynchronous Distributed Training. 2019-10-24
- High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
- SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
- Detailed comparison of communication efficiency of split learning and federated learning
- Decentralized Federated Learning: A Segmented Gossip Approach
- Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation
- One-Shot Federated Learning
- Multi-objective Evolutionary Federated Learning
- Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
- FedOpt: Towards communication efficiency and privacy preservation in federated learning
- A performance evaluation of federated learning algorithms
- Federated Learning: Strategies for Improving Communication Efficiency
- Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training. ICLR 2018. 2017-12-05
- NeurIPS 2020 submission: Artemis: tight convergence guarantees for bidirectional compression in Federated Learning. 2020-06-25
- Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC. 2020-06-21
- (x) Federated Mutual Learning. 2020-06-27
- Federated Learning With Quantized Global Model Updates. 2020-06-18
- Evaluating the Communication Efficiency in Federated Learning Algorithm. 2020-04-06
- Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning. 2020-05-21
- Ternary Compression for Communication-Efficient Federated Learning. 2020-05-07
- Gradient Statistics Aware Power Control for Over-the-Air Federated Learning. 2020-05-04
- Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection. 2020-02-22
- (*) RPN: A Residual Pooling Network for Efficient Federated Learning. ECAI 2020.
- Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning. 2020-01-22
- L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning
- Federated Learning with Compression: Unified Analysis and Sharp Guarantees. 2020-07-02
- Partitioned Variational Inference: A unified framework encompassing federated and continual learning
- Gradient Sparification for Asynchronous Distributed Training. 2019-10-24
- High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
-
-
Straggler Problem
-
NeurIPS
- Coded Federated Learning. Presented at the Wireless Edge Intelligence Workshop, IEEE GLOBECOM 2019
- Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
- Coded Federated Computing in Wireless Networks with Straggling Devices and Imperfect CSI
- Information-Theoretic Perspective of Federated Learning
-
-
Computation Efficiency
-
NeurIPS
- NeurIPS 2020 Submission: Distributed Learning on Heterogeneous Resource-Constrained Devices
- SplitFed: When Federated Learning Meets Split Learning
- Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning
- Secure Federated Learning in 5G Mobile Networks. 2020/04
- Asynchronous Online Federated Learning for Edge Devices
- (*) Secure Federated Submodel Learning
- Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence
- Model Pruning Enables Efficient Federated Learning on Edge Devices
- Towards Effective Device-Aware Federated Learning
- Accelerating DNN Training in Wireless Federated Edge Learning System
- Split learning for health: Distributed deep learning without sharing raw patient data
- SmartPC: Hierarchical pace control in real-time federated learning system
- DeCaf: Iterative collaborative processing over the edge
- Accelerating DNN Training in Wireless Federated Edge Learning System
-
-
Wireless Communication and Cloud Computing
-
NeurIPS
- Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
- Wireless Communications for Collaborative Federated Learning in the Internet of Things
- Democratizing the Edge: A Pervasive Edge Computing Framework
- UVeQFed: Universal Vector Quantization for Federated Learning
- Federated Deep Learning Framework For Hybrid Beamforming in mm-Wave Massive MIMO
- Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints
- A Secure Federated Learning Framework for 5G Networks
- Federated Learning and Wireless Communications
- Lightwave Power Transfer for Federated Learning-based Wireless Networks
- Towards Ubiquitous AI in 6G with Federated Learning
- Optimizing Over-the-Air Computation in IRS-Aided C-RAN Systems
- Network-Aware Optimization of Distributed Learning for Fog Computing
- On the Design of Communication Efficient Federated Learning over Wireless Networks
- Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
- Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective
- Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach
- A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus
- Scheduling for Cellular Federated Edge Learning with Importance and Channel. 2020-04
- Differentially Private Federated Learning for Resource-Constrained Internet of Things. 2020-03
- Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks. 2020-03
- Gradient Estimation for Federated Learning over Massive MIMO Communication Systems
- Adaptive Federated Learning With Gradient Compression in Uplink NOMA
- Performance Analysis and Optimization in Privacy-Preserving Federated Learning
- Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design
- Federated Over-the-Air Subspace Learning and Tracking from Incomplete Data
- Decentralized Federated Learning via SGD over Wireless D2D Networks
- Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
- Wireless Federated Learning with Local Differential Privacy
- Learning from Peers at the Wireless Edge
- Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge
- Communication Efficient Federated Learning over Multiple Access Channels
- Convergence Time Optimization for Federated Learning over Wireless Networks
- One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis
- Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks. IEEE Internet of Things Journal. 2020
- Asynchronous Federated Learning with Differential Privacy for Edge Intelligence
- Federated learning with multichannel ALOHA
- Federated Learning with Autotuned Communication-Efficient Secure Aggregation
- Bandwidth Slicing to Boost Federated Learning in Edge Computing
- Energy Efficient Federated Learning Over Wireless Communication Networks
- Device Scheduling with Fast Convergence for Wireless Federated Learning
- Energy-Aware Analog Aggregation for Federated Learning with Redundant Data
- Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks
- Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
- Federated Learning over Wireless Networks: Optimization Model Design and Analysis
- Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach
- Reliable Federated Learning for Mobile Networks
- FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
- Active Federated Learning
- Cell-Free Massive MIMO for Wireless Federated Learning
- A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
- On Safeguarding Privacy and Security in the Framework of Federated Learning
- Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges
- Scheduling Policies for Federated Learning in Wireless Networks
- Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs
- Federated Learning over Wireless Fading Channels
- Energy-Efficient Radio Resource Allocation for Federated Edge Learning
- Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System
- Active Learning Solution on Distributed Edge Computing
- Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
- Federated Learning via Over-the-Air Computation
- Broadband Analog Aggregation for Low-Latency Federated Edge Learning
- Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks
- Joint Service Pricing and Cooperative Relay Communication for Federated Learning
- In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
- Asynchronous Task Allocation for Federated and Parallelized Mobile Edge Learning
- Optimizing Over-the-Air Computation in IRS-Aided C-RAN Systems
- Network-Aware Optimization of Distributed Learning for Fog Computing
- On the Design of Communication Efficient Federated Learning over Wireless Networks
- Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
- Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective
- Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach
- A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus
- Scheduling for Cellular Federated Edge Learning with Importance and Channel. 2020-04
- Differentially Private Federated Learning for Resource-Constrained Internet of Things. 2020-03
- Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks. 2020-03
- Gradient Estimation for Federated Learning over Massive MIMO Communication Systems
- Adaptive Federated Learning With Gradient Compression in Uplink NOMA
- Performance Analysis and Optimization in Privacy-Preserving Federated Learning
- Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design
- Federated Over-the-Air Subspace Learning and Tracking from Incomplete Data
- Decentralized Federated Learning via SGD over Wireless D2D Networks
- HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
- Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
- Wireless Federated Learning with Local Differential Privacy
- Learning from Peers at the Wireless Edge
- Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge
- Communication Efficient Federated Learning over Multiple Access Channels
- Convergence Time Optimization for Federated Learning over Wireless Networks
- One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis
- Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks. IEEE Internet of Things Journal. 2020
- Asynchronous Federated Learning with Differential Privacy for Edge Intelligence
- Federated learning with multichannel ALOHA
- Federated Learning with Autotuned Communication-Efficient Secure Aggregation
- Bandwidth Slicing to Boost Federated Learning in Edge Computing
- Energy Efficient Federated Learning Over Wireless Communication Networks
- Device Scheduling with Fast Convergence for Wireless Federated Learning
- Energy-Aware Analog Aggregation for Federated Learning with Redundant Data
- Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks
- Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
- Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach
- Reliable Federated Learning for Mobile Networks
- FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
- Active Federated Learning
- Cell-Free Massive MIMO for Wireless Federated Learning
- A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
- On Safeguarding Privacy and Security in the Framework of Federated Learning
- Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges
- Scheduling Policies for Federated Learning in Wireless Networks
- Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs
- Federated Learning over Wireless Fading Channels
- Energy-Efficient Radio Resource Allocation for Federated Edge Learning
- Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System
- Active Learning Solution on Distributed Edge Computing
- Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
- Federated Learning via Over-the-Air Computation
- Broadband Analog Aggregation for Low-Latency Federated Edge Learning
- Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks
- Joint Service Pricing and Cooperative Relay Communication for Federated Learning
- In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
- Asynchronous Task Allocation for Federated and Parallelized Mobile Edge Learning
- HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
- Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation. 2020-02
- Fast Uplink Grant for NOMA: a Federated Learning based Approach
- Hierarchical Federated Learning Across Heterogeneous Cellular Networks
- Towards Ubiquitous AI in 6G with Federated Learning
- Hierarchical Federated Learning Across Heterogeneous Cellular Networks
- Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation. 2020-02
- Fast Uplink Grant for NOMA: a Federated Learning based Approach
-
-
FL System Design
-
NeurIPS
- Towards Federated Learning at Scale: System Design
- FedML: A Research Library and Benchmark for Federated Machine Learning
- FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction
- Heterogeneity-Aware Federated Learning
- Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification
- [startup
- (startup) Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
- (*) TiFL: A Tier-based Federated Learning System. HPDC 2020 (High-Performance Parallel and Distributed Computing).
- Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach. ICDCS 2020 (2020 International Conference on Distributed Computing Systems)
- Quantifying the Performance of Federated Transfer Learning
- Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices
- Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning
- BAFFLE : Blockchain Based Aggregator Free Federated Learning
- Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking
- HierTrain: Fast Hierarchical Edge AI Learning With Hybrid Parallelism in Mobile-Edge-Cloud Computing
- Towards Federated Learning at Scale: System Design
- FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction
- Heterogeneity-Aware Federated Learning
- Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification
- [startup
- (startup) Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
- (*) TiFL: A Tier-based Federated Learning System. HPDC 2020 (High-Performance Parallel and Distributed Computing).
- Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach. ICDCS 2020 (2020 International Conference on Distributed Computing Systems)
- Quantifying the Performance of Federated Transfer Learning
- Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices
- Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning
- BAFFLE : Blockchain Based Aggregator Free Federated Learning
- Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking
- HierTrain: Fast Hierarchical Edge AI Learning With Hybrid Parallelism in Mobile-Edge-Cloud Computing
- ELFISH: Resource-Aware Federated Learning on Heterogeneous Edge Devices
- (FL startup: Tongdun, HangZhou, China) Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework. 2020-02
- FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC. ICDCS 2020 (2020 International Conference on Distributed Computing Systems)
- FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC. ICDCS 2020 (2020 International Conference on Distributed Computing Systems)
- (FL startup: Tongdun, HangZhou, China) Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework. 2020-02
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-
Models
-
Graph Neural Networks
- Privacy-Preserving Graph Neural Network for Node Classification
- Cluster-driven Graph Federated Learning over Multiple Domains
- FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
- FedGL: Federated Graph Learning Framework with Global Self-Supervision
- SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
- A Vertical Federated Learning Framework for Graph Convolutional Network
- Federated Myopic Community Detection with One-shot Communication
- Federated Graph Classification over Non-IID Graphs
- Subgraph Federated Learning with Missing Neighbor Generation
- Federated Dynamic GNN with Secure Aggregation
- Peer-to-peer federated learning on graphs
- Towards Federated Graph Learning for Collaborative Financial Crimes Detection
- A Graph Federated Architecture with Privacy Preserving Learning
- Privacy-Preserving Graph Neural Network for Node Classification
- ASFGNN: Automated Separated-Federated Graph Neural Network
- GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
- FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
- FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search
- Federated Graph Learning -- A Position Paper
- Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
-
Generative Models (GAN, Bayesian Generative Models, etc)
- Discrete-Time Cox Models
- Generative Models for Effective ML on Private, Decentralized Datasets. Google. ICLR 2020
- MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets. 2018-11-09
- (GAN) Federated Generative Adversarial Learning. 2020-05-07
- Differentially Private Data Generative Models
- GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model
- Discrete-Time Cox Models
- Generative Models for Effective ML on Private, Decentralized Datasets. Google. ICLR 2020
- MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets. 2018-11-09
- (GAN) Federated Generative Adversarial Learning. 2020-05-07
- Differentially Private Data Generative Models
- GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model
-
VAE (Variational Autoencoder)
-
MF (Matrix Factorization)
- Secure Federated Matrix Factorization
- (Clustering) Federated Clustering via Matrix Factorization Models: From Model Averaging to Gradient Sharing
- Privacy Threats Against Federated Matrix Factorization
- (Clustering) Federated Clustering via Matrix Factorization Models: From Model Averaging to Gradient Sharing
- Privacy Threats Against Federated Matrix Factorization
-
GBDT (Gradient Boosting Decision Trees)
- Practical Federated Gradient Boosting Decision Trees. AAAI 2020.
- Federated Extra-Trees with Privacy Preserving
- SecureGBM: Secure Multi-Party Gradient Boosting
- Federated Forest
- The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost
- Practical Federated Gradient Boosting Decision Trees. AAAI 2020.
- Federated Extra-Trees with Privacy Preserving
- SecureGBM: Secure Multi-Party Gradient Boosting
- Federated Forest
- The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost
-
Other Model
- Privacy Preserving QoE Modeling using Collaborative Learning
- Distributed Dual Coordinate Ascent in General Tree Networks and Its Application in Federated Learning
- Privacy Preserving QoE Modeling using Collaborative Learning
- Distributed Dual Coordinate Ascent in General Tree Networks and Its Application in Federated Learning
-
Federated Learning on Knowledge Graphs
-
-
Natural language Processing
-
Other Model
- Federated pretraining and fine tuning of BERT using clinical notes from multiple silos
- Federated Learning for Mobile Keyboard Prediction
- Federated Learning for Keyword Spotting
- Pretraining Federated Text Models for Next Word Prediction
- FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning. MSRA. 2020-03.
- Federated Learning of N-gram Language Models. Google. ACL 2019.
- Federated User Representation Learning
- Two-stage Federated Phenotyping and Patient Representation Learning
- Federated Learning for Emoji Prediction in a Mobile Keyboard
- Federated AI lets a team imagine together: Federated Learning of GANs
- Federated Learning Of Out-Of-Vocabulary Words
- Learning Private Neural Language Modeling with Attentive Aggregation
- Applied Federated Learning: Improving Google Keyboard Query Suggestions
- Federated Learning for Ranking Browser History Suggestions
- Federated pretraining and fine tuning of BERT using clinical notes from multiple silos
- Federated Learning for Mobile Keyboard Prediction
- Federated Learning for Keyword Spotting
- Pretraining Federated Text Models for Next Word Prediction
- FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning. MSRA. 2020-03.
- Learning Private Neural Language Modeling with Attentive Aggregation
- Federated User Representation Learning
- Two-stage Federated Phenotyping and Patient Representation Learning
- Federated Learning for Emoji Prediction in a Mobile Keyboard
- Federated AI lets a team imagine together: Federated Learning of GANs
- Federated Learning Of Out-Of-Vocabulary Words
- Applied Federated Learning: Improving Google Keyboard Query Suggestions
- Federated Learning for Ranking Browser History Suggestions
-
-
Computer Vision
-
Other Model
- Federated Face Anti-spoofing
- (*) Federated Visual Classification with Real-World Data Distribution. MIT. ECCV 2020. 2020-03
- FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
- Federated Face Anti-spoofing
- (*) Federated Visual Classification with Real-World Data Distribution. MIT. ECCV 2020. 2020-03
- FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
-
-
Health Care:
-
Other Model
- Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
- Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data
- Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
- A Federated Learning Framework for Healthcare IoT devices
- Federated Transfer Learning for EEG Signal Classification
- The Future of Digital Health with Federated Learning
- Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records
- Stratified cross-validation for unbiased and privacy-preserving federated learning
- Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
- Learn Electronic Health Records by Fully Decentralized Federated Learning
- Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality
- Federated and Differentially Private Learning for Electronic Health Records
- A blockchain-orchestrated Federated Learning architecture for healthcare consortia
- Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
- Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving
- Differential Privacy-enabled Federated Learning for Sensitive Health Data
- LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
- Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning
- Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence
- Privacy-preserving Federated Brain Tumour Segmentation
- HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
- FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
- Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
- LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical Data
- FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
- Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
- Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data
- Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
- A Federated Learning Framework for Healthcare IoT devices
- Federated Transfer Learning for EEG Signal Classification
- The Future of Digital Health with Federated Learning
- Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records
- Stratified cross-validation for unbiased and privacy-preserving federated learning
- Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
- Learn Electronic Health Records by Fully Decentralized Federated Learning
- Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality
- Federated and Differentially Private Learning for Electronic Health Records
- A blockchain-orchestrated Federated Learning architecture for healthcare consortia
- Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
- Differential Privacy-enabled Federated Learning for Sensitive Health Data
- Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence
- Privacy-preserving Federated Brain Tumour Segmentation
- HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
- FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
- Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
- LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical Data
- FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
- Anonymizing Data for Privacy-Preserving Federated Learning. ECAI 2020.
- Anonymizing Data for Privacy-Preserving Federated Learning. ECAI 2020.
-
-
Transportation:
-
Other Model
- Federated Learning for Vehicular Networks
- Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach
- Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks
- Beyond privacy regulations: an ethical approach to data usage in transportation. TomTom. 2020-04-01
- Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
- Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory. 2020-03
- FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing. 2020-03
- Practical Privacy Preserving POI Recommendation
- Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method
- Federated Transfer Reinforcement Learning for Autonomous Driving
- Energy Demand Prediction with Federated Learning for Electric Vehicle Networks
- Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications
- Federated Learning for Ultra-Reliable Low-Latency V2V Communications
- Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach
- Beyond privacy regulations: an ethical approach to data usage in transportation. TomTom. 2020-04-01
- Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
- Practical Privacy Preserving POI Recommendation
- Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method
- Federated Transfer Reinforcement Learning for Autonomous Driving
- Energy Demand Prediction with Federated Learning for Electric Vehicle Networks
- Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications
- Federated Learning for Ultra-Reliable Low-Latency V2V Communications
- Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach
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-
Recommendation System
-
Other Model
- (*) Federated Multi-view Matrix Factorization for Personalized Recommendations
- Robust Federated Recommendation System
- Federated Recommendation System via Differential Privacy
- FedRec: Privacy-Preserving News Recommendation with Federated Learning. MSRA. 2020-03
- Federating Recommendations Using Differentially Private Prototypes
- Meta Matrix Factorization for Federated Rating Predictions
- Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
- Federated Hierarchical Hybrid Networks for Clickbait Detection
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-
Speech Recognition
-
Finance
-
Smart City
-
Robotics
-
Other Model
- Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
- Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
- Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
- Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
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-
Networking
-
Blockchain
-
Other
-
Other Model
- Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing
- Self-supervised audio representation learning for mobile devices
- Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics
- PMF: A Privacy-preserving Human Mobility Prediction Framework via Federated Learning
- Federated Multi-task Hierarchical Attention Model for Sensor Analytics
- DÏoT: A Federated Self-learning Anomaly Detection System for IoT
-
-
Benchmark and Dataset
-
Other Model
- The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
- Evaluation Framework For Large-scale Federated Learning
- (*) PrivacyFL: A simulator for privacy-preserving and secure federated learning. MIT CSAIL.
- Revocable Federated Learning: A Benchmark of Federated Forest
- Real-World Image Datasets for Federated Learning
- LEAF: A Benchmark for Federated Settings
- Functional Federated Learning in Erlang (ffl-erl)
- The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
- Evaluation Framework For Large-scale Federated Learning
- (*) PrivacyFL: A simulator for privacy-preserving and secure federated learning. MIT CSAIL.
- Revocable Federated Learning: A Benchmark of Federated Forest
- Real-World Image Datasets for Federated Learning
- LEAF: A Benchmark for Federated Settings
- Functional Federated Learning in Erlang (ffl-erl)
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-
Survey
-
Other Model
- Qinbin Li, PhD, NUS, HKUST
- SECure: A Social and Environmental Certificate for AI Systems
- From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks
- Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
- A Review of Privacy Preserving Federated Learning for Private IoT Analytics
- Threats to Federated Learning: A Survey
- Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective
- Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
- Advances and Open Problems in Federated Learning
- Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing
- An Introduction to Communication Efficient Edge Machine Learning
- Federated Learning for Coalition Operations
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey
- Federated Learning: Challenges, Methods, and Future Directions
- Federated Machine Learning: Concept and Applications
- No Peek: A Survey of private distributed deep learning
- Communication-Efficient Edge AI: Algorithms and Systems
- SECure: A Social and Environmental Certificate for AI Systems
- From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks
- Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
- A Review of Privacy Preserving Federated Learning for Private IoT Analytics
- Threats to Federated Learning: A Survey
- Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective
- Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
- Advances and Open Problems in Federated Learning
- Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing
- An Introduction to Communication Efficient Edge Machine Learning
- Federated Learning for Coalition Operations
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey
- Federated Learning: Challenges, Methods, and Future Directions
- Federated Machine Learning: Concept and Applications
- No Peek: A Survey of private distributed deep learning
- Communication-Efficient Edge AI: Algorithms and Systems
- A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
- Federated Learning for Healthcare Informatics
- Federated Learning for Healthcare Informatics
- A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
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Domain Adaptation
Categories
Wireless Communication and Cloud Computing
128
Distributed optimization
123
Non-IID and Model Personalization
87
Adversarial Attack and Defense
78
Privacy
63
Models
56
Health Care:
49
Communication Efficiency
45
Survey
37
FL System Design
34
Natural language Processing
27
Transportation:
23
Vertical Federated Learning
17
Computation Efficiency
14
Benchmark and Dataset
14
Transfer Learning
13
Decentralized FL
11
Incentive Mechanism
10
Neural Architecture Search
9
Recommendation System
8
Other
6
Fairness
6
Computer Vision
6
Semi-Supervised Learning
6
Hierarchical FL
6
Publications in Top-tier ML/CV/NLP/DM Conference (ICML, NeurIPS, ICLR, CVPR, ACL, AAAI, KDD)
4
Straggler Problem
4
Robotics
4
Federated Learning Library - FedML https://fedml.ai
3
Networking
2
Continual Learning
2
Finance
2
Smart City
2
Bayesian Learning
2
Domain Adaptation
2
Blockchain
2
Reinforcement Learning
2
Causal Learning
1
Speech Recognition
1