Awesome Federated Machine Learning 
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 Computer Vision Conferences
- Top Artificial Intelligence and Data Mining Conferences
- Books
- Papers (Research directions)
- Google FL Workshops
- Videos and Lectures
- Tutorials and Blogs
- Open-Sources
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
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
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
Personalized Federated Learning under Mixture of Distributions
University of California, Los Angeles
Federated Heavy Hitter Recovery under Linear Sketching
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
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
The Chinese University of Hong Kong
Federated Online and Bandit Convex Optimization
Toyota Technology Institute
Federated Linear Contextual Bandits with User-level Differential Privacy
The Pennsylvania State University
Shanghai Jiao Tong University
TabLeak: Tabular Data Leakage in Federated Learning
ETH Zurich
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
Fast Federated Machine Unlearning with Nonlinear Functional Theory
Auburn University
Efficient Personalized Federated Learning via Sparse Model-Adaptation
Alibaba
DoCoFL: Downlink Compression for Cross-Device Federated Learning
VMware Research
Private Federated Learning with Autotuned Compression
The Johns Hopkins University
Personalized Subgraph Federated Learning
KAIST
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
Hong Kong University of Science and Technology
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
FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization
Alibaba
Federated Hypergradient Computation via Aggregated Iterative Differentiation
University at Buffalo
Personalized Federated Learning with Inferred Collaboration Graphs
Shanghai Jiao Tong University
Secure Federated Correlation Test and Entropy Estimation
Carnegie Mellon University
Doubly Adversarial Federated Bandits
London School of Economics and Political Science
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
Shanghai Jiao Tong University
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
Federated Adversarial Learning: A Framework with Convergence Analysis
University of British Columbia
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
FeDXL: Provable Federated Learning for Deep X-Risk Optimization
Texas A&M University
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
Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships
Jilin University
Federated Conformal Predictors for Distributed Uncertainty Quantification
MIT
Shanghai Jiao Tong University
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization
KAIST
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
University of Oulu
FedNL: Making Newton-Type Methods Applicable to Federated Learning
KAUST
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
Carnegie Mellon University
FedNest: Federated Bilevel, Minimax, and Compositional Optimization
University of Michigan
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
University of Maryland
University of Science and Technology of China
Federated Learning with Positive and Unlabeled Data
Xi’an Jiaotong University
Neurotoxin: Durable Backdoors in Federated Learning
Southeast University;
Princeton University
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning
University of Cambridge
Neural Tangent Kernel Empowered Federated Learning
NC State University
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning
VMware Research
Architecture Agnostic Federated Learning for Neural Networks
The University of Texas at Austin
Fast Composite Optimization and Statistical Recovery in Federated Learning
Shanghai Jiao Tong University
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
New York University
Communication-Efficient Adaptive Federated Learning
Pennsylvania State University
Personalized Federated Learning via Variational Bayesian Inference
Chinese Academy of Sciences
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning
Nankai University
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
University of Minnesota
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation
Stanford University;
Google Research
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
Stanford University;
Google Research
University of Science and Technology of China
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling
Geogia Institute of Technology
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering
University of Michigan
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems
Michigan State University
Accelerated Federated Learning with Decoupled Adaptive Optimization
Auburn University
Proximal and Federated Random Reshuffling
KAUST
Personalized Federated Learning through Local Memorization
Inria
Federated Learning with Partial Model Personalization
University of Washington
CISPA Helmholz Center for Information Security
Federated Learning with Label Distribution Skew via Logits Calibration
Zhejiang University
The Ohio State University
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
Hong Kong Baptist University
Generalized Federated Learning via Sharpness Aware Minimization
University of South Florida
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
University of Michigan
Multi-Level Branched Regularization for Federated Learning
Seoul National University
Harvard University
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis
Peking University;
Princeton University
Personalized Federated Learning using Hypernetworks
Bar-Ilan University;
NVIDIA
Federated Composite Optimization
Stanford University;
Google
Exploiting Shared Representations for Personalized Federated Learning
University of Texas at Austin;
University of Pennsylvania
Data-Free Knowledge Distillation for Heterogeneous Federated Learning
Michigan State University
Federated Continual Learning with Weighted Inter-client Transfer
KAIST
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity
The University of Iowa
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
The University of Tokyo
Federated Learning of User Verification Models Without Sharing Embeddings
Qualcomm
Accenture
Ditto: Fair and Robust Federated Learning Through Personalization
CMU;
Facebook AI
Heterogeneity for the Win: One-Shot Federated Clustering
CMU
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
Debiasing Model Updates for Improving Personalized Federated Training
Boston University;
Arm
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
Toyota;
Berkeley;
Cornell University
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks
UIUC;
IBM
Federated Learning under Arbitrary Communication Patterns
Indiana University;
Amazon
FedBoost: A Communication-Efficient Algorithm for Federated Learning
FetchSGD: Communication-Efficient Federated Learning with Sketching
UC Berkeley;
Johns Hopkins University;
Amazon
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
EPFL;
Google
Federated Learning with Only Positive Labels
From Local SGD to Local Fixed-Point Methods for Federated Learning
Moscow Institute of Physics and Technology;
KAUST
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
KAUST
Bayesian Nonparametric Federated Learning of Neural Networks
IBM
Analyzing Federated Learning through an Adversarial Lens
Princeton University;
IBM
ICLR
Years
Title
Affiliation
Materials
MocoSFL: enabling cross-client collaborative self-supervised learning
Arizona State University
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
Michigan State University
KAUST
National University of Singapore
Machine Unlearning of Federated Clusters
University of Illinois Urbana-Champaign
FedFA: Federated Feature Augmentation
ETH Zurich
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
Carnegie Mellon University
Better Generative Replay for Continual Federated Learning
University of Virginia
Federated Learning from Small Datasets
University Hospital Essen
Federated Nearest Neighbor Machine Translation
University of Science and Technology of China
Test-Time Robust Personalization for Federated Learning
Westlake University
DepthFL : Depthwise Federated Learning for Heterogeneous Clients
Seoul National University
Towards Addressing Label Skews in One-Shot Federated Learning
National University of Singapore
Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning
National University of Singapore
Bias Propagation in Federated Learning
National University of Singapore
University of Maryland
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
University of Maryland
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
University of Southern California
Purdue University
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy
The University of Sydney
University of Cambridge
Multimodal Federated Learning via Contrastive Representation Ensemble
Tsinghua University
Faster federated optimization under second-order similarity
Princeton University
ETH Zurich
The University of Texas at Austin
PerFedMask: Personalized Federated Learning with Optimized Masking Vectors
The University of British Columbia
FedDAR: Federated Domain-Aware Representation Learning
Harvard University
George Mason University
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
Purdue University
Renmin University of China
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
Hong Kong Baptist University
Efficient Federated Domain Translation
Purdue University
On the Importance and Applicability of Pre-Training for Federated Learning
The Ohio State University
University of California, Los Angeles
Instance-wise Batch Label Restoration via Gradients in Federated Learning
Beihang University
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
Sparse Random Networks for Communication-Efficient Federated Learning
Stanford University
Hyperparameter Optimization through Neural Network Partitioning
University of Cambridge
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?
MIT CSAIL
KAUST
Bayesian Framework for Gradient Leakage
ETH Zurich
Federated Learning from only unlabeled data with class-conditional-sharing clients
The University of Tokyo;
The Chinese University of Hong Kong
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
FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning
POSTECH
An Agnostic Approach to Federated Learning with Class Imbalance
University of Pennsylvania
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization
Michigan State University;
The University of Texas at Austin
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
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
Purdue University
University of Maryland;
Google
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
FedBABU: Toward Enhanced Representation for Federated Image Classification
KAIST
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
EPFL
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters
Aibee
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
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond
KAIST;
MIT
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
FedMix: Approximation of Mixup under Mean Augmented Federated Learning
KAIST
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
CMU; Google
Adaptive Federated Optimization
Personalized Federated Learning with First Order Model Optimization
Stanford University; NVIDIA
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Princeton University
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
The Ohio State University
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
KAIST
Federated Adversarial Domain Adaptation
Boston University;
Columbia University;
Rutgers University
DBA: Distributed Backdoor Attacks against Federated Learning
Zhejiang University;
IBM Research
Fair Resource Allocation in Federated Learning
CMU;
Facebook AI
Federated Learning with Matched Averaging
University of Wisconsin-Madison;
IBM Research
Differentially Private Meta-Learning
CMU
Generative Models for Effective ML on Private, Decentralized Datasets
On the Convergence of FedAvg on Non-IID Data
Peking University
NeurIPS
Years
Title
Affiliation
Materials
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
University at Buffalo
Multiply Robust Federated Estimation of Targeted Average Treatment Effects
Northeastern University
Incentivized Communication for Federated Bandits
University of Virginia
Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization
Rutgers University
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
Johns Hopkins University
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition
Mohamed Bin Zayed University of Artificial Intelligence
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
Beijing University of Posts and Telecommunications
Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization
University of Illinois, Urbana-Champaign
Federated Linear Bandits with Finite Adversarial Actions
University of Virginia
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
KAIST
IBA: Towards Irreversible Backdoor Attacks in Federated Learning
Vanderbilt University
Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning
University of Technology Sydney
University of Southern California
Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Federated Object Detection
KAIST
Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization
Huazhong Agricultural University
Guiding The Last Layer in Federated Learning with Pre-Trained Models
Concordia University
FedNAR: Federated Optimization with Normalized Annealing Regularization
Mohamed bin Zayed University of Artificial Intelligence
One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning
Rice University
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training
Georgia Institute of Technology
FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning
The Pennsylvania State University
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
The Pennsylvania State University
The George Washington University
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
East China Normal University
A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning
Western University
RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks
Xidian University
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
Shanghai Jiao Tong University
George Mason University
FedL2P: Federated Learning to Personalize
University of Cambridge
Adaptive Test-Time Personalization for Federated Learning
University of Illinois Urbana-Champaign
Federated Conditional Stochastic Optimization
University of Pittsburgh
Federated Spectral Clustering via Secure Similarity Reconstruction
The Chinese University of Hong Kong
University of Michigan
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
Carnegie Mellon University
FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout
University of British Columbia
Flow: Per-instance Personalized Federated Learning
University of Massachusetts
A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning
The Pennsylvania State University
Federated Compositional Deep AUC Maximization
Temple University
DELTA: Diverse Client Sampling for Fasting Federated Learning
The Chinese University of Hong Kong
Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization
The University of Sydney
StableFDG: Style and Attention Based Learning for Federated Domain Generalization