Awesome-Federated-Machine-Learning

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
  • ICML ICLR NeurIPS
• Top Computer Vision Conferences
  • CVPR ICCV ECCV
• Top Artificial Intelligence and Data Mining Conferences
  • AAAI AISTATS KDD
• Books
• Papers (Research directions)
  • Model Aggregation
  • Personalization  
  • Recommender system
  • Security  
  • Survey  
  • Efficiency  
  • Optimization  
  • Fairness
    
  • Application  
  • Boosting  
  • Incentive mechanism
  • Unsupervised Learning  
  • Heterogeneity  
  • Client Selection
  • Graph Neural Networks
  • Other Machine Learning Paradigm
  • Computational Learning Theory
• 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 2023	A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates	Accenture Labs
	Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning	ESAT-PSI, KU Leuven	code
	LeadFL: Client Self-Defense against Model Poisoning in Federated Learning	Delft University of Technology
	Achieving Linear Speedup in Non-IID Federated Bilevel Learning	Meta
	On the Convergence of Federated Averaging with Cyclic Client Participation	Carnegie Mellon University
	FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models	The Hong Kong University of Science and Technology
	Flash: Concept Drift Adaptation in Federated Learning	University of Massachusetts	code
	Personalized Federated Learning under Mixture of Distributions	University of California, Los Angeles
	Federated Heavy Hitter Recovery under Linear Sketching	Google
	Improving the Model Consistency of Decentralized Federated Learning	Tsinghua University
	Towards Unbiased Training in Federated Open-world Semi-supervised Learning	The Hong Kong Polytechnic University
	Optimizing the Collaboration Structure in Cross-Silo Federated Learning	University of Illinois Urbana-Champaign	code
	FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction	The Chinese University of Hong Kong	code
	Federated Online and Bandit Convex Optimization	Toyota Technology Institute
	Federated Linear Contextual Bandits with User-level Differential Privacy	The Pennsylvania State University
	FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization	Shanghai Jiao Tong University	code
	TabLeak: Tabular Data Leakage in Federated Learning	ETH Zurich
	Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis	Georgia Institute of Technology
	The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond	Carnegie Mellon University
	LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning	Rensselaer Polytechnic Institute
	One-Shot Federated Conformal Prediction	Universite Paris-Saclay	code
	Fast Federated Machine Unlearning with Nonlinear Functional Theory	Auburn University	code
	Efficient Personalized Federated Learning via Sparse Model-Adaptation	Alibaba	code
	DoCoFL: Downlink Compression for Cross-Device Federated Learning	VMware Research
	Private Federated Learning with Autotuned Compression	The Johns Hopkins University
	Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Linear Speedup and Partial Participation	LinkedIn
	Personalized Subgraph Federated Learning	KAIST	code
	Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning	Hong Kong University of Science and Technology	code
	Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape	The University of Sydney
	Towards Understanding Ensemble Distillation in Federated Learning	KAIST
	SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning	Owkin Inc	code
	FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization	Alibaba	code
	Federated Hypergradient Computation via Aggregated Iterative Differentiation	University at Buffalo
	Personalized Federated Learning with Inferred Collaboration Graphs	Shanghai Jiao Tong University	code
	Secure Federated Correlation Test and Entropy Estimation	Carnegie Mellon University	code
	Doubly Adversarial Federated Bandits	London School of Economics and Political Science
	FedDisco: Federated Learning with Discrepancy-Aware Collaboration	Shanghai Jiao Tong University	code
	Revisiting Weighted Aggregation in Federated Learning with Neural Networks	Zhejiang University
	Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design	Meta
	Anchor Sampling for Federated Learning with Partial Client Participation	Purdue University	code
	Federated Adversarial Learning: A Framework with Convergence Analysis	University of British Columbia
	Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction	Duke University
	GuardHFL: Privacy Guardian for Heterogeneous Federated Learning	University of Electronic Science and Technology of China
	Vertical Federated Graph Neural Network for Recommender System	National University of Singapore	code
	FeDXL: Provable Federated Learning for Deep X-Risk Optimization	Texas A&M University	code
	Conformal Prediction for Federated Uncertainty Quantification Under Label Shift	Lagrange Mathematics and Computing Research Center
	From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning	Univ. Lille, Inria
	No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation	Harbin Institute of Technology	code
	Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships	Jilin University	code
	Federated Conformal Predictors for Distributed Uncertainty Quantification	MIT	code
ICML 2022	Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning	Shanghai Jiao Tong University	code video
	Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization	KAIST	video slide
	FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning	University of Oulu	code video slide
	FedNL: Making Newton-Type Methods Applicable to Federated Learning	KAUST	video slide video
	Federated Minimax Optimization: Improved Convergence Analyses and Algorithms	Carnegie Mellon University	slide video
	FedNest: Federated Bilevel, Minimax, and Compositional Optimization	University of Michigan	code video slide
	Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification	University of Maryland	code slide video
	DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training	University of Science and Technology of China	code video
	Federated Learning with Positive and Unlabeled Data	Xi’an Jiaotong University	video
	Neurotoxin: Durable Backdoors in Federated Learning	Southeast University; Princeton University	code slide video
	DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning	University of Cambridge	slide video
	Neural Tangent Kernel Empowered Federated Learning	NC State University	code slide video
	EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning	VMware Research	code slide video
	Architecture Agnostic Federated Learning for Neural Networks	The University of Texas at Austin	slide video
	Fast Composite Optimization and Statistical Recovery in Federated Learning	Shanghai Jiao Tong University	slide video
	Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning	New York University	slide video
	Communication-Efficient Adaptive Federated Learning	Pennsylvania State University	slide video
	Personalized Federated Learning via Variational Bayesian Inference	Chinese Academy of Sciences	code video
	QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning	Nankai University	code slide video
	Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy	University of Minnesota	video
	The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation	Stanford University; Google Research	slide video
	The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning	Stanford University; Google Research	code video
	Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring	University of Science and Technology of China	slide video
	Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling	Geogia Institute of Technology	slide video
	Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering	University of Michigan	code video
	Resilient and Communication Efficient Learning for Heterogeneous Federated Systems	Michigan State University	slide video
	Accelerated Federated Learning with Decoupled Adaptive Optimization	Auburn University	slide video
	Proximal and Federated Random Reshuffling	KAUST	code video
	Personalized Federated Learning through Local Memorization	Inria	code video
	Federated Learning with Partial Model Personalization	University of Washington	code slide video
	ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training	CISPA Helmholz Center for Information Security	code video
	Federated Learning with Label Distribution Skew via Logits Calibration	Zhejiang University	slide video
	Anarchic Federated Learning	The Ohio State University	slide video
	Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning	Hong Kong Baptist University	code video
	Generalized Federated Learning via Sharpness Aware Minimization	University of South Florida	slide video
	FedScale: Benchmarking Model and System Performance of Federated Learning at Scale	University of Michigan	code video
	Multi-Level Branched Regularization for Federated Learning	Seoul National University	HomePage slide video
ICML 2021	Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix	Harvard University	video code
	FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis	Peking University; Princeton University	video
	Personalized Federated Learning using Hypernetworks	Bar-Ilan University; NVIDIA	code HomePage video
	Federated Composite Optimization	Stanford University; Google	code video slides
	Exploiting Shared Representations for Personalized Federated Learning	University of Texas at Austin; University of Pennsylvania	code video
	Data-Free Knowledge Distillation for Heterogeneous Federated Learning	Michigan State University	code video
	Federated Continual Learning with Weighted Inter-client Transfer	KAIST	code video
	Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity	The University of Iowa	video
	Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning	The University of Tokyo	video
	Federated Learning of User Verification Models Without Sharing Embeddings	Qualcomm	video
	Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning	Accenture	code video
	Ditto: Fair and Robust Federated Learning Through Personalization	CMU; Facebook AI	code video
	Heterogeneity for the Win: One-Shot Federated Clustering	CMU	video
	The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation	Google	video
	Debiasing Model Updates for Improving Personalized Federated Training	Boston University; Arm	video
	One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning	Toyota; Berkeley; Cornell University	code video
	CRFL: Certifiably Robust Federated Learning against Backdoor Attacks	UIUC; IBM	code video
	Federated Learning under Arbitrary Communication Patterns	Indiana University; Amazon	video
ICML 2020	FedBoost: A Communication-Efficient Algorithm for Federated Learning	Google	Video
	FetchSGD: Communication-Efficient Federated Learning with Sketching	UC Berkeley; Johns Hopkins University; Amazon	Video Code
	SCAFFOLD: Stochastic Controlled Averaging for Federated Learning	EPFL; Google	Video
	Federated Learning with Only Positive Labels	Google	Video
	From Local SGD to Local Fixed-Point Methods for Federated Learning	Moscow Institute of Physics and Technology; KAUST	Slide Video
	Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization	KAUST	Slide Video
ICML 2019	Bayesian Nonparametric Federated Learning of Neural Networks	IBM	Code
	Analyzing Federated Learning through an Adversarial Lens	Princeton University; IBM	Code
	Agnostic Federated Learning	Google

ICLR

Years	Title	Affiliation	Materials
ICLR 2023	MocoSFL: enabling cross-client collaborative self-supervised learning	Arizona State University	Code
	Personalized Federated Learning with Feature Alignment and Classifier Collaboration	Tsinghua University
	Single-shot General Hyper-parameter Optimization for Federated Learning	IBM Research

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