开源软件名称（OpenSource Name）：

innovation-cat/Awesome-Federated-Machine-Learning

开源软件地址(OpenSource Url)：

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

开源编程语言(OpenSource Language)：

开源软件介绍(OpenSource Introduction)：

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.

Top Machine Learning Conferences
- ICML ICLR NeurIPS
Top Computer Vision Conferences
- CVPR ICCV ECCV
Top Artificial Intelligence and Data Mining Conferences
- AAAI AISTATS KDD
Books
Papers (Research directions)
Google FL Workshops
Videos and Lectures
Tutorials and Blogs
Open-Sources
- Enterprise Grade
- Research Purpose

Top Machine Learning Conferences

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

ICML

Years	Title	Affiliations	Materials
ICML 2022	Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning	Shanghai Jiao Tong University	code
	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	code
	FedNL: Making Newton-Type Methods Applicable to Federated Learning	KAUST	video
	Federated Minimax Optimization: Improved Convergence Analyses and Algorithms	Carnegie Mellon University
	FedNest: Federated Bilevel, Minimax, and Compositional Optimization	University of Michigan	code
	Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification	University of Maryland	code
	DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training	University of Science and Technology of China	code
	Federated Learning with Positive and Unlabeled Data	Xi’an Jiaotong University
	Neurotoxin: Durable Backdoors in Federated Learning	Southeast University; Princeton University	code
	DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning	University of Cambridge
	Neural Tangent Kernel Empowered Federated Learning	NC State University	code
	EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning	VMware Research	code
	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	code
	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	code
	Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring	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	code
	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	code
	Personalized Federated Learning through Local Memorization	Inria	code
	Federated Learning with Partial Model Personalization	University of Washington	code
	ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training	CISPA Helmholz Center for Information Security	code
	Federated Learning with Label Distribution Skew via Logits Calibration	Zhejiang University
	Anarchic Federated Learning	The Ohio State University
	Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning	Hong Kong Baptist University	code
	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	code
	Multi-Level Branched Regularization for Federated Learning	Seoul National University	HomePage

ICML 2021	Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix	Harvard University	video code
	FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis	Peking University; Princeton University	video
	Personalized Federated Learning using Hypernetworks	Bar-Ilan University; NVIDIA	code HomePage video
	Federated Composite Optimization	Stanford University; Google	code video slides
	Exploiting Shared Representations for Personalized Federated Learning	University of Texas at Austin; University of Pennsylvania	code video
	Data-Free Knowledge Distillation for Heterogeneous Federated Learning	Michigan State University	code video
	Federated Continual Learning with Weighted Inter-client Transfer	KAIST	code video
	Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity	The University of Iowa	video
	Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning	The University of Tokyo	video
	Federated Learning of User Verification Models Without Sharing Embeddings	Qualcomm	video
	Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning	Accenture	code video
	Ditto: Fair and Robust Federated Learning Through Personalization	CMU; Facebook AI	code video
	Heterogeneity for the Win: One-Shot Federated Clustering	CMU	video
	The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation	Google	video
	Debiasing Model Updates for Improving Personalized Federated Training	Boston University; Arm	video
	One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning	Toyota; Berkeley; Cornell University	code video
	CRFL: Certifiably Robust Federated Learning against Backdoor Attacks	UIUC; IBM	code video
	Federated Learning under Arbitrary Communication Patterns	Indiana University; Amazon	video

ICML 2020	FedBoost: A Communication-Efficient Algorithm for Federated Learning	Google	Video
	FetchSGD: Communication-Efficient Federated Learning with Sketching	UC Berkeley; Johns Hopkins University; Amazon	Video Code
	SCAFFOLD: Stochastic Controlled Averaging for Federated Learning	EPFL; Google	Video
	Federated Learning with Only Positive Labels	Google	Video
	From Local SGD to Local Fixed-Point Methods for Federated Learning	Moscow Institute of Physics and Technology; KAUST	Slide Video
	Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization	KAUST	Slide Video

ICML 2019	Bayesian Nonparametric Federated Learning of Neural Networks	IBM	Code
	Analyzing Federated Learning through an Adversarial Lens	Princeton University; IBM	Code
	Agnostic Federated Learning	Google

ICLR

Years	Title	Affiliation	Materials
ICLR 2022	Bayesian Framework for Gradient Leakage	ETH Zurich	Code
	Federated Learning from only unlabeled data with class-conditional-sharing clients	The University of Tokyo; The Chinese University of Hong Kong	Code
	FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning	Carnegie Mellon University; University of Illinois at Urbana-Champaign; University of Washington
	Acceleration of Federated Learning with Alleviated Forgetting in Local Training	Tsinghua University	Code
	FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning	POSTECH	Code
	An Agnostic Approach to Federated Learning with Class Imbalance	University of Pennsylvania	Code
	Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization	Michigan State University; The University of Texas at Austin	code
	Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models	University of Maryland; New York University	code (Minimum) code (Comprehensive)
	ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity	University of Cambridge; University of Oxford
	Diverse Client Selection for Federated Learning via Submodular Maximization	Intel; Carnegie Mellon University	code
	Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?	Purdue University	code
	Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions	University of Maryland; Google	code
	Towards Model Agnostic Federated Learning Using Knowledge Distillation	EPFL
	Divergence-aware Federated Self-Supervised Learning	Nanyang Technological University; SenseTime
	What Do We Mean by Generalization in Federated Learning?	Stanford University; Google	code
	FedBABU: Toward Enhanced Representation for Federated Image Classification	KAIST	code
	Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing	EPFL	code
	Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters	Aibee	code
	Hybrid Local SGD for Federated Learning with Heterogeneous Communications	University of Texas; Pennsylvania State University
	On Bridging Generic and Personalized Federated Learning for Image Classification	The Ohio State University	code
	Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond	KAIST; MIT

ICLR 2021	Federated Learning Based on Dynamic Regularization	Boston University; ARM
	Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning	The Ohio State University
	HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients	Duke University	code
	FedMix: Approximation of Mixup under Mean Augmented Federated Learning	KAIST
	Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms	CMU; Google	code
	Adaptive Federated Optimization	Google	code
	Personalized Federated Learning with First Order Model Optimization	Stanford University; NVIDIA
	FedBN: Federated Learning on Non-IID Features via Local Batch Normalization	Princeton University	code
	FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning	The Ohio State University
	Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning	KAIST	code

ICLR 2020	Federated Adversarial Domain Adaptation	Boston University; Columbia University; Rutgers University
	DBA: Distributed Backdoor Attacks against Federated Learning	Zhejiang University; IBM Research	Code
	Fair Resource Allocation in Federated Learning	CMU; Facebook AI	Code
	Federated Learning with Matched Averaging	University of Wisconsin-Madison; IBM Research	Code
	Differentially Private Meta-Learning	CMU
	Generative Models for Effective ML on Private, Decentralized Datasets	Google	Code
	On the Convergence of FedAvg on Non-IID Data	Peking University	Code

NeurIPS

Years	Title	Affiliation	Materials
NeurIPS 2021	Sageflow: Robust Federated Learning against Both Stragglers and Adversaries	KAIST	HomePage
	CAFE: Catastrophic Data Leakage in Vertical Federated Learning	Rensselaer Polytechnic Institute; IBM Research	code HomePage
	Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee	NUS	code HomePage
	Optimality and Stability in Federated Learning: A Game-theoretic Approach	Cornell University	code HomePage
	QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning	UCLA	HomePage
	The Skellam Mechanism for Differentially Private Federated Learning	Google Research; CMU	HomePage
	No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data	NUS; Huawei	HomePage
	STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning	University of Minnesota	HomePage
	Subgraph Federated Learning with Missing Neighbor Generation	Emory University; University of British Columbia; Lehigh University	HomePage
	Evaluating Gradient Inversion Attacks and Defenses in Federated Learning	Princeton University	Code HomePage
	Personalized Federated Learning With Gaussian Processes	Bar-Ilan University	code HomePage
	Differentially Private Federated Bayesian Optimization with Distributed Exploration	MIT; NUS	code HomePage
	Parameterized Knowledge Transfer for Personalized Federated Learning	Hong Kong Polytechnic University;	HomePage
	Federated Reconstruction: Partially Local Federated Learning	Google Research	HomePage
	Fast Federated Learning in the Presence of Arbitrary Device Unavailability	Tsinghua University; Princeton University; MIT	code HomePage
	FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective	Duke University; Accenture Labs	code HomePage
	FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout	KAUST; Samsung AI Center	HomePage
	Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients	University of Pennsylvania	HomePage
	Federated Multi-Task Learning under a Mixture of Distributions	INRIA; Accenture Labs	code HomePage
	Federated Graph Classification over Non-IID Graphs	Emory University	HomePage
	Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing	CMU; Hewlett Packard Enterprise	code HomePage
	On Large-Cohort Training for Federated Learning	Google; CMU	code HomePage
	DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning	KAUST; Columbia University; University of Central Florida	code HomePage
	PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization	Huawei	HomePage
	Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis	KAIST	HomePage
	Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning	Tsinghua University; Alibaba; Weill Cornell Medicine	code HomePage
	Federated Linear Contextual Bandits	The Pennsylvania State University; Facebook; University of Virginia	HomePage
	Few-Round Learning for Federated Learning	KAIST	HomePage
	Breaking the centralized barrier for cross-device federated learning	EPFL; Google Research	code HomePage
	Federated-EM with heterogeneity mitigation and variance reduction	Ecole Polytechnique; Google Research	HomePage
	Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning	MIT; Amazon; Google	HomePage
	FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization	University of North Carolina at Chapel Hill; IBM Research	code HomePage
	Gradient Inversion with Generative Image Prior	Pohang University of Science and Technology; University of Wisconsin-Madison; University of Washington	code HomePage

NeurIPS 2020	Differentially-Private Federated Linear Bandits	MIT	code
	Federated Principal Component Analysis	University of Cambridge; Quine Technologies	code
	FedSplit: an algorithmic framework for fast federated optimization	UC Berkeley
	Federated Bayesian Optimization via Thompson Sampling	NUS; MIT