/Awesome-Federated-Learning-on-Graph-and-Tabular-Data

Federated learning on graph and tabular data related papers, frameworks, and datasets.

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Federated-Learning-on-Graph-and-Tabular-Data

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Table of Contents

Papers

keywords

Statistics: 🔥 code is available & stars >= 100 | ⭐ citation >= 50 | 🎓 Top-tier venue

kg.: Knowledge Graph | data.: dataset  |   surv.: survey

Update log

  • 2022/07/22 - add CVPR 2022 and MM 2020,2021 papers

  • 2022/07/21 - give TL;DR and interpret information of papers. And add KDD 2022 papers

  • 2022/07/15 - give a list of papers in the field of federated learning in top NLP/Secure conferences. And add ICML 2022 papers

  • 2022/07/14 - give a list of papers in the field of federated learning in top ML/CV/AI/DM conferences from innovation-cat‘s’ Awesome-Federated-Machine-Learning and find 🔥 papers(code is available & stars >= 100)

  • 2022/07/12 - added information about the last commit time of the federated learning open source framework (can be used to determine the maintenance of the code base)

  • 2022/07/12 - give a list of papers in the field of federated learning in top journals

  • 2022/05/25 - complete the paper and code lists of FL on tabular data and Tree algorithms

  • 2022/05/25 - add the paper list of FL on tabular data and Tree algorithms

  • 2022/05/24 - complete the paper and code lists of FL on graph data and Graph Neural Networks

  • 2022/05/23 - add the paper list of FL on graph data and Graph Neural Networks

  • 2022/05/21 - update all of Federated Learning Framework

FL on Graph Data and Graph Neural Networks

dblp

This section partially refers to DBLP search engine and repositories Awesome-Federated-Learning-on-Graph-and-GNN-papers and Awesome-Federated-Machine-Learning.

Title Affiliation Venue Year TL;DR Materials
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy Shanghai Jiao Tong University KDD 🎓 2022 FedWalk1 [PDF]
EasyFGL: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning 🔥 Alibaba Group KDD 🎓 2022 FederatedScope-GNN 2 [PDF] Code
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning ICML 🎓 2022 [PUB.] [Code]
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting kg. IJCAI 🎓 2022 [PDF] [Code]
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks AAAI 🎓 2022 [PDF] [Code] [Interpret(zh)]
A federated graph neural network framework for privacy-preserving personalization Nature Communications 2022 [PUB.]
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation kg. ACL Workshop 2022 [PDF] [Code]
Power Allocation for Wireless Federated Learning using Graph Neural Networks ICASSP 2022 [PDF] [PUB.] [Code]
Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization ICASSP 2022 [PUB.]
Federated Graph Learning with Periodic Neighbour Sampling IWQoS 2022 [PUB.]
A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy KSEM 2022 [PDF] [PUB.]
Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning WCNC 2022 [PUB.]
Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning INFOCOM Workshops 2022 [PUB.]
Federated learning of molecular properties with graph neural networks in a heterogeneous setting Patterns 2022 [PUB.]
Decentralized Graph Federated Multitask Learning for Streaming Data CISS 2022 [PUB.] [[Interpret(zh)]]
Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications JBHI 2022 [PDF]
Federated Knowledge Graphs Embedding kg. CIKM 2021 [PDF] [Code] [Interpret(zh)]
Federated Graph Classification over Non-IID Graphs NeurIPS 🎓 2021 [PDF] [PUB.] [Code] [Interpret(zh)]
Decentralized Federated Graph Neural Networks IJCAI Workshop 2021 [PDF]
FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper ICCAD 2021 [PUB.]
DAG-FL: Direct Acyclic Graph-based Blockchain Empowers On-Device Federated Learning ICC 2021 [PUB.]
Graphical Federated Cloud Sharing Markets TSUSC 2021 [PUB.]
Virtual Knowledge Graphs for Federated Log Analysis kg. ARES 2021 [PUB.]
FedE: Embedding Knowledge Graphs in Federated Setting kg. IJCKG 2021 [PDF] [PUB.][Code]
Federated Knowledge Graph Embeddings with Heterogeneous Data kg. CCKS 2021 [PUB.]
A Graph Federated Architecture with Privacy Preserving Learning SPAWC 2021 [PDF] [PUB.]
Federated Social Recommendation with Graph Neural Network ACM TIST 2021 [PDF]
Subgraph Federated Learning with Missing Neighbor Generation NeurIPS 🎓 2021 [PDF] [PUB.]
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling KDD 🎓 2021 [PDF] [Code]
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks 🔥 surv. ICLR-DPML 2021 [PDF] [Code] [Interpret(zh)]
Cluster-driven Graph Federated Learning over Multiple Domains CVPR Workshop 2021 [PDF] [Interpret(zh)]
Differentially Private Federated Knowledge Graphs Embedding kg. CIKM 2021 [PDF] [Code]
Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling IWQoS 2021 [PUB.]
A Federated Multigraph Integration Approach for Connectional Brain Template Learning MICCAI Workshop 2021 [PDF]
FedGraph: Federated Graph Learning with Intelligent Sampling TPDS 🎓 2021 [PDF]
Federated Graph Neural Network for Cross-graph Node Classification CCIS 2021 [PUB.]
GraFeHTy: Graph Neural Network using Federated Learning for Human Activity Recognition ICMLA 2021 [PUB.]
Distributed Training of Graph Convolutional Networks TSIPN 2021 [PUB.] [PDF] [[Interpret(zh)]]
ASFGNN: Automated Separated-Federated Graph Neural Network PPNA 2020 [PDF] [PUB.] [Interpret(zh)]
Towards Federated Graph Learning for Collaborative Financial Crimes Detection NeurIPS Workshop 2019 [PDF]
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation ICML workshop 2021 [PDF] [Interpret(zh)]
SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure BigData 2019 [PDF] [PUB.]
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications surv. University of Virginia preprint 2022 FGML 3 [PDF]
FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR preprint 2022 [PDF]
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning preprint 2022 [PDF]
Personalized Subgraph Federated Learning preprint 2022 [PDF]
Federated Graph Attention Network for Rumor Detection preprint 2022 [PDF] [Code]
FedRel: An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning preprint 2022 [PDF]
Privatized Graph Federated Learning preprint 2022 [PDF]
Graph-Assisted Communication-Efficient Ensemble Federated Learning preprint 2022 [PDF]
Federated Graph Neural Networks: Overview, Techniques and Challenges surv. preprint 2022 [PDF]
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks preprint 2022 [PDF]
FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks preprint 2022 [PDF] [Code]
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks preprint 2022 [PDF]
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction preprint 2021 [PDF]
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks preprint 2021 [PDF] [Code]
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning preprint 2021 [PDF] [Code]
Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries kg. preprint 2021 [PDF]
Federated Myopic Community Detection with One-shot Communication preprint 2021 [PDF]
Federated Graph Learning -- A Position Paper surv. preprint 2021 [PDF]
A Vertical Federated Learning Framework for Graph Convolutional Network preprint 2021 [PDF]
FedGL: Federated Graph Learning Framework with Global Self-Supervision preprint 2021 [PDF]
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search preprint 2021 [PDF]
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach preprint 2021 [PDF]
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs preprint 2020 [PDF]
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty kg. preprint 2020 [PDF]
Federated Dynamic GNN with Secure Aggregation preprint 2020 [PDF]
GraphFederator: Federated Visual Analysis for Multi-party Graphs preprint 2020 [PDF]
Privacy-Preserving Graph Neural Network for Node Classification preprint 2020 [PDF]
Peer-to-peer federated learning on graphs preprint 2019 [PDF]

Private Graph Neural Networks (todo)

  • [Arxiv 2021] Privacy-Preserving Graph Convolutional Networks for Text Classification. [PDF]
  • [Arxiv 2021] GraphMI: Extracting Private Graph Data from Graph Neural Networks. [PDF]
  • [Arxiv 2021] Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning. [PDF]
  • [Arxiv 2020] Locally Private Graph Neural Networks. [PDF]

FL on Tabular Data

dblp

This section refers to DBLP search engine.

Title Affiliation Venue Year TL;DR Materials
Federated Random Forests can improve local performance of predictive models for various healthcare applications Bioinform. 2022 [PUB.] [Code]
Federated Forest TBD 2022 [PDF] [PUB.]
Federated Functional Gradient Boosting AISTATS 2022 [PDF] [PUB.] [Code]
Fed-GBM: a cost-effective federated gradient boosting tree for non-intrusive load monitoring e-Energy 2022 [PUB.]
eFL-Boost: Efficient Federated Learning for Gradient Boosting Decision Trees IEEE Access 2022 [PUB.]
Random Forest Based on Federated Learning for Intrusion Detection AIAI 2022 [PUB.]
Cross-silo federated learning based decision trees SAC 2022 [PUB.]
Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning INFCOM Workshops 2022 [PUB.]
VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning SIGMOD 🎓 2021 [PUB.]
An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee TPDS 🎓 2021 [PDF] [PUB.]
A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network Intrusion Detection System IEEE Access 2021 [PUB.]
Research on privacy protection of multi source data based on improved gbdt federated ensemble method with different metrics Phys. Commun. 2021 [PUB.]
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Vertical Federated Learning IEEE BigData 2021 [PDF] [PUB.]
Gradient Boosting Forest: a Two-Stage Ensemble Method Enabling Federated Learning of GBDTs ICONIP 2021 [PUB.]
A k-Anonymised Federated Learning Framework with Decision Trees DPM/CBT @ESORICS 2021 [PUB.]
AF-DNDF: Asynchronous Federated Learning of Deep Neural Decision Forests SEAA 2021 [PUB.]
Compression Boosts Differentially Private Federated Learning EuroS&P 2021 [PDF] [PUB.]
Practical Federated Gradient Boosting Decision Trees AAAI 🎓 2020 [PDF] [PUB.] [Code]
Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing ICDCS 2020 [PDF] [PUB.]
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling IEEE BigData 2020 [PDF] [PUB.]
Bandwidth Slicing to Boost Federated Learning Over Passive Optical Networks IEEE Communications Letters 2020 [PUB.]
Privacy Preserving Vertical Federated Learning for Tree-based Models Proc. VLDB Endow. 2020 [PDF] [PUB.]
DFedForest: Decentralized Federated Forest Blockchain 2020 [PUB.]
Straggler Remission for Federated Learning via Decentralized Redundant Cayley Tree LATINCOM 2020 [PUB.]
Federated Soft Gradient Boosting Machine for Streaming Data Federated Learning 2020 [PUB.]
Federated Learning of Deep Neural Decision Forests LOD 2019 [PUB.]
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems preprint 2022 [PDF]
Hercules: Boosting the Performance of Privacy-preserving Federated Learning preprint 2022 [PDF]
FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging preprint 2022 [PDF]
An Efficient and Robust System for Vertically Federated Random Forest preprint 2022 [PDF]
Guess what? You can boost Federated Learning for free preprint 2021 [PDF]
SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning 🔥 preprint 2021 [PDF] [Code]
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data preprint 2021 [PDF]
A Tree-based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources preprint 2021 [PDF] [Code]
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning preprint 2020 [PDF]
FederBoost: Private Federated Learning for GBDT preprint 2020 [PDF]
Privacy Preserving Text Recognition with Gradient-Boosting for Federated Learning preprint 2020 [PDF] [Code]
Cloud-based Federated Boosting for Mobile Crowdsensing preprint 2020 [arxiv]
Federated Extra-Trees with Privacy Preserving preprint 2020 [PDF]
Bandwidth Slicing to Boost Federated Learning in Edge Computing preprint 2019 [PDF]
Revocable Federated Learning: A Benchmark of Federated Forest preprint 2019 [PDF]

FL in top-tier journal

List of papers in the field of federated learning in Nature(and its sub-journals), Cell, Science(and Science Advances) and PANS refers to WOS search engine.

Title Affiliation Venue Year TL;DR Materials
Shifting machine learning for healthcare from development to deployment and from models to data Nat. Biomed. Eng 2022 [PUB.]
Communication-efficient federated learning via knowledge distillation Nat Commun 2022 [PUB.]
A federated graph neural network framework for privacy-preserving personalization Nat Commun 2022 [PUB.] [Code]
Swarm Learning for decentralized and confidential clinical machine learning Nature 🎓 2021 [PUB.]
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning Nat. Mach. Intell. 2021 [PUB.]
End-to-end privacy preserving deep learning on multi-institutional medical imaging Nat. Mach. Intell. 2021 [PUB.]
Federated learning for predicting clinical outcomes in patients with COVID-19 Nat Med 2021 [PUB.]
Communication-efficient federated learning PANS 2021 [PUB.] [Code]
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence Nat. Mach. Intell. 2021 [PUB.]
Secure, privacy-preserving and federated machine learning in medical imaging Nat Mach Intell 2020 [PUB.]
Breaking medical data sharing boundaries by using synthesized radiographs Science Advances 2020 [PUB.]

FL in top AI Conferences

In this section, we will summarize Federated Learning papers accepted by top AI(Artificial Intelligence) conference, Including AAAI(AAAI Conference on Artificial Intelligence), AISTATS(Artificial Intelligence and Statistics).

Title Affiliation Venue Year TL;DR Materials
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images The Chinese University of Hong Kong; Beihang University AAAI 2022 [Code]
Federated Learning for Face Recognition with Gradient Correction Beijing University of Posts and Telecommunications AAAI 2022
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data university of Southern California AAAI 2022 [Code]
SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures Harbin Institute of Technology; Peng Cheng Laboratory AAAI 2022
Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network Tianjin University AAAI 2022
Seizing Critical Learning Periods in Federated Learning SUNY-Binghamton University; Louisiana State University AAAI 2022
Coordinating Momenta for Cross-silo Federated Learning University of Pittsburgh AAAI 2022
FedProto: Federated Prototype Learning over Heterogeneous Devices University of Technology Sydney; University of Washington AAAI 2022 [Code]
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating Carnegie Mellon University AAAI 2022
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better The University of Texas at Austin AAAI 2022 [Code]
FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition National Taiwan University AAAI 2022 [Code]
SplitFed: When Federated Learning Meets Split Learning CSIRO; Lehigh University AAAI 2022 [Code]
Efficient Device Scheduling with Multi-Job Federated Learning Soochow University; Baidu AAAI 2022
Implicit Gradient Alignment in Distributed and Federated Learning IIT Kanpur; EPFL AAAI 2022
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies IBM Research; Wichita State University AAAI 2022
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating Xidian University; JD Tech AAAI 2021 video
FedRec++: Lossless Federated Recommendation with Explicit Feedback Shenzhen University AAAI 2021 video
Federated Multi-Armed Bandits University of Virginia AAAI 2021 [Code] video
On the Convergence of Communication-Efficient Local SGD for Federated Learning Temple University; University of Pittsburgh AAAI 2021 video
FLAME: Differentially Private Federated Learning in the Shuffle Model Renmin University of China; Kyoto University AAAI 2021 video [Code]
Toward Understanding the Influence of Individual Clients in Federated Learning Shanghai Jiao Tong University; The University of Texas at Dallas AAAI 2021 video
Provably Secure Federated Learning against Malicious Clients Duke University AAAI 2021 video slides
Personalized Cross-Silo Federated Learning on Non-IID Data Simon Fraser University; McMaster University AAAI 2021 video
Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation Cornell University AAAI 2021 [Code] video
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning University of Nevada; IBM Research AAAI 2021 video
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning IIT Bombay; IBM Research AAAI 2021 video Supplementary
Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models The Chinese University of Hong Kong; Arizona State University AAAI 2021 video [Code]
Addressing Class Imbalance in Federated Learning Northwestern University AAAI 2021 video [Code] [[Interpret(zh)]]
Defending against Backdoors in Federated Learning with Robust Learning Rate The University of Texas at Dallas AAAI 2021 video [Code]
Free-rider Attacks on Model Aggregation in Federated Learning Accenture Labs AISTAT 2021 video Supplementary
Federated f-differential privacy University of Pennsylvania AISTAT 2021 [Code] video Supplementary
Federated learning with compression: Unified analysis and sharp guarantees 🔥 The Pennsylvania State University; The University of Texas at Austin AISTAT 2021 [Code] video Supplementary
Shuffled Model of Differential Privacy in Federated Learning UCLA; Google AISTAT 2021 video Supplementary
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning Google AISTAT 2021 video Supplementary
Federated Multi-armed Bandits with Personalization University of Virginia; The Pennsylvania State University AISTAT 2021 [Code] video Supplementary
Towards Flexible Device Participation in Federated Learning CMU; Sun Yat-Sen University AISTAT 2021 video Supplementary
Practical Federated Gradient Boosting Decision Trees National University of Singapore; The University of Western Australia AAAI 2020 [Code]
Federated Learning for Vision-and-Language Grounding Problems Peking University; Tencent AAAI 2020
Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework Beihang University AAAI 2020
Federated Patient Hashing Cornell University AAAI 2020
Robust Federated Learning via Collaborative Machine Teaching Symantec Research Labs; KAUST AAAI 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization UC Santa Barbara; UT Austin AISTAT 2020 video Supplementary
How To Backdoor Federated Learning 🔥 Cornell Tech AISTAT 2020 video [Code] Supplementary
Federated Heavy Hitters Discovery with Differential Privacy RPI; Google AISTAT 2020 video Supplementary

FL in top ML conferences

In this section, we will summarize Federated Learning papers accepted by top ML(machine learning) conference, Including NeurIPS(Annual Conference on Neural Information Processing Systems), ICML(International Conference on Machine Learning), ICLR(International Conference on Learning Representations).

Title Affiliation Venue Year TL;DR Materials
Fast Composite Optimization and Statistical Recovery in Federated Learning Shanghai Jiao Tong University ICML 2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning New York University ICML 2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning 🔥 Stanford University; Google Research ICML 2022 code slides
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation Stanford University; Google Research ICML 2022
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training University of Science and Technology of China ICML 2022 code
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning University of Oulu ICML 2022 code
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning University of Cambridge ICML 2022 slides
Accelerated Federated Learning with Decoupled Adaptive Optimization Auburn University ICML 2022
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling Geogia Institute of Technology ICML 2022
Multi-Level Branched Regularization for Federated Learning Seoul National University ICML 2022 HomePage
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale 🔥 University of Michigan ICML 2022 code
Federated Learning with Positive and Unlabeled Data Xi’an Jiaotong University ICML 2022
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning Shanghai Jiao Tong University ICML 2022 code
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering University of Michigan ICML 2022 code
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring University of Science and Technology of China ICML 2022 slides
Architecture Agnostic Federated Learning for Neural Networks The University of Texas at Austin ICML 2022
Personalized Federated Learning through Local Memorization Inria ICML 2022 code
Proximal and Federated Random Reshuffling KAUST ICML 2022 code
Federated Learning with Partial Model Personalization University of Washington ICML 2022 code
Generalized Federated Learning via Sharpness Aware Minimization University of South Florida ICML 2022
FedNL: Making Newton-Type Methods Applicable to Federated Learning KAUST ICML 2022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Carnegie Mellon University ICML 2022 slides
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning Hong Kong Baptist University ICML 2022 code
FedNest: Federated Bilevel, Minimax, and Compositional Optimization University of Michigan ICML 2022 code
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning VMware Research ICML 2022 code
Communication-Efficient Adaptive Federated Learning Pennsylvania State University ICML 2022
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training CISPA Helmholz Center for Information Security ICML 2022 code
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification 🔥 University of Maryland ICML 2022 code
Anarchic Federated Learning The Ohio State University ICML 2022
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning Nankai University ICML 2022 code
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization KAIST ICML 2022
Neural Tangent Kernel Empowered Federated Learning NC State University ICML 2022 code
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy University of Minnesota ICML 2022
Personalized Federated Learning via Variational Bayesian Inference Chinese Academy of Sciences ICML 2022
Federated Learning with Label Distribution Skew via Logits Calibration Zhejiang University ICML 2022
Neurotoxin: Durable Backdoors in Federated Learning Southeast University;Princeton University ICML 2022 code
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems Michigan State University ICML 2022
Bayesian Framework for Gradient Leakage ETH Zurich ICLR 2022 [Code]
Federated Learning from only unlabeled data with class-conditional-sharing clients The University of Tokyo; The Chinese University of Hong Kong ICLR 2022 [Code]
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning Carnegie Mellon University; University of Illinois at Urbana-Champaign; University of Washington ICLR 2022
Acceleration of Federated Learning with Alleviated Forgetting in Local Training Tsinghua University ICLR 2022 [Code]
FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning POSTECH ICLR 2022 [Code]
An Agnostic Approach to Federated Learning with Class Imbalance University of Pennsylvania ICLR 2022 [Code]
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization Michigan State University; The University of Texas at Austin ICLR 2022 [Code]
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models 🔥 University of Maryland; New York University ICLR 2022 [Code] (Minimum) [Code] (Comprehensive)
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity University of Cambridge; University of Oxford ICLR 2022
Diverse Client Selection for Federated Learning via Submodular Maximization Intel; Carnegie Mellon University ICLR 2022 [Code]
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? Purdue University ICLR 2022 [Code]
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions 🔥 University of Maryland; Google ICLR 2022 [Code]
Towards Model Agnostic Federated Learning Using Knowledge Distillation EPFL ICLR 2022
Divergence-aware Federated Self-Supervised Learning Nanyang Technological University; SenseTime ICLR 2022
What Do We Mean by Generalization in Federated Learning? 🔥 Stanford University; Google ICLR 2022 [Code]
FedBABU: Toward Enhanced Representation for Federated Image Classification KAIST ICLR 2022 [Code]
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing EPFL ICLR 2022 [Code]
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters Aibee ICLR Spotlight 2022 Homepage
Hybrid Local SGD for Federated Learning with Heterogeneous Communications University of Texas; Pennsylvania State University ICLR 2022
On Bridging Generic and Personalized Federated Learning for Image Classification The Ohio State University ICLR 2022 [Code]
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond KAIST; MIT ICLR 2022
Federated Learning Based on Dynamic Regularization Boston University; ARM ICLR 2021
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning The Ohio State University ICLR 2021
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients Duke University ICLR 2021 [Code]
FedMix: Approximation of Mixup under Mean Augmented Federated Learning KAIST ICLR 2021
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms 🔥 CMU; Google ICLR 2021 [Code]
Adaptive Federated Optimization Google ICLR 2021 [Code]
Personalized Federated Learning with First Order Model Optimization Stanford University; NVIDIA ICLR 2021
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization 🔥 Princeton University ICLR 2021 [Code]
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning The Ohio State University ICLR 2021
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning KAIST ICLR 2021 [Code]
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix Harvard University ICML 2021 video [Code]
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis Peking University; Princeton University ICML 2021 video
Personalized Federated Learning using Hypernetworks 🔥 Bar-Ilan University; NVIDIA ICML 2021 [Code] HomePage video [Interpret(zh)]
Federated Composite Optimization Stanford University; Google ICML 2021 [Code] video slides
Exploiting Shared Representations for Personalized Federated Learning University of Texas at Austin; University of Pennsylvania ICML 2021 [Code] video
Data-Free Knowledge Distillation for Heterogeneous Federated Learning 🔥 Michigan State University ICML 2021 [Code] video
Federated Continual Learning with Weighted Inter-client Transfer KAIST ICML 2021 [Code] video
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity The University of Iowa ICML 2021 video
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning The University of Tokyo ICML 2021 video
Federated Learning of User Verification Models Without Sharing Embeddings Qualcomm ICML 2021 video
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning Accenture ICML 2021 [Code] video
Ditto: Fair and Robust Federated Learning Through Personalization CMU; Facebook AI ICML 2021 [Code] video
Heterogeneity for the Win: One-Shot Federated Clustering CMU ICML 2021 video
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Google ICML 2021 video
Debiasing Model Updates for Improving Personalized Federated Training Boston University; Arm ICML 2021 video
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning Toyota; Berkeley; Cornell University ICML 2021 [Code] video
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks UIUC; IBM ICML 2021 [Code] video
Federated Learning under Arbitrary Communication Patterns Indiana University; Amazon ICML 2021 video
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries KAIST NeurIPS 2021 HomePage
CAFE: Catastrophic Data Leakage in Vertical Federated Learning Rensselaer Polytechnic Institute; IBM Research NeurIPS 2021 [Code] HomePage
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee NUS NeurIPS 2021 [Code] HomePage
Optimality and Stability in Federated Learning: A Game-theoretic Approach Cornell University NeurIPS 2021 [Code] HomePage
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning UCLA NeurIPS 2021 HomePage [Code] [Interpret(zh)]
The Skellam Mechanism for Differentially Private Federated Learning Google Research; CMU NeurIPS 2021 HomePage
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data NUS; Huawei NeurIPS 2021 HomePage
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning University of Minnesota NeurIPS 2021 HomePage
Subgraph Federated Learning with Missing Neighbor Generation Emory University; University of British Columbia; Lehigh University NeurIPS 2021 HomePage
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning Princeton University NeurIPS 2021 HomePage
Personalized Federated Learning With Gaussian Processes Bar-Ilan University NeurIPS 2021 [Code] HomePage
Differentially Private Federated Bayesian Optimization with Distributed Exploration MIT; NUS NeurIPS 2021 [Code] HomePage
Parameterized Knowledge Transfer for Personalized Federated Learning Hong Kong Polytechnic University; NeurIPS 2021 HomePage
Federated Reconstruction: Partially Local Federated Learning Google Research NeurIPS 2021 HomePage
Fast Federated Learning in the Presence of Arbitrary Device Unavailability Tsinghua University; Princeton University; MIT NeurIPS 2021 [Code] HomePage
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective Duke University; Accenture Labs NeurIPS 2021 [Code] HomePage
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout KAUST; Samsung AI Center NeurIPS 2021 HomePage
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients University of Pennsylvania NeurIPS 2021 HomePage
Federated Multi-Task Learning under a Mixture of Distributions INRIA; Accenture Labs NeurIPS 2021 [Code] HomePage
Federated Graph Classification over Non-IID Graphs Emory University NeurIPS 2021 HomePage
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing CMU; Hewlett Packard Enterprise NeurIPS 2021 [Code] HomePage
On Large-Cohort Training for Federated Learning 🔥 Google; CMU NeurIPS 2021 [Code] HomePage
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning KAUST; Columbia University; University of Central Florida NeurIPS 2021 [Code] HomePage
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization Huawei NeurIPS 2021 HomePage
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis KAIST NeurIPS 2021 HomePage
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning Tsinghua University; Alibaba; Weill Cornell Medicine NeurIPS 2021 [Code] HomePage
Federated Linear Contextual Bandits The Pennsylvania State University; Facebook; University of Virginia NeurIPS 2021 HomePage
Few-Round Learning for Federated Learning KAIST NeurIPS 2021 HomePage
Breaking the centralized barrier for cross-device federated learning EPFL; Google Research NeurIPS 2021 [Code] HomePage Video
Federated-EM with heterogeneity mitigation and variance reduction Ecole Polytechnique; Google Research NeurIPS 2021 HomePage
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning MIT; Amazon; Google NeurIPS 2021 HomePage
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization University of North Carolina at Chapel Hill; IBM Research NeurIPS 2021 [Code] HomePage
Gradient Inversion with Generative Image Prior Pohang University of Science and Technology; University of Wisconsin-Madison; University of Washington NeurIPS 2021 [Code] HomePage
Federated Adversarial Domain Adaptation Boston University; Columbia University; Rutgers University ICLR 2020
DBA: Distributed Backdoor Attacks against Federated Learning Zhejiang University; IBM Research ICLR 2020 [Code]
Fair Resource Allocation in Federated Learning 🔥 CMU; Facebook AI ICLR 2020 [Code]
Federated Learning with Matched Averaging 🔥 University of Wisconsin-Madison; IBM Research ICLR 2020 [Code]
Differentially Private Meta-Learning CMU ICLR 2020
Generative Models for Effective ML on Private, Decentralized Datasets 🔥 Google ICLR 2020 [Code]
On the Convergence of FedAvg on Non-IID Data 🔥 Peking University ICLR 2020 [Code] [Interpret(zh)]
FedBoost: A Communication-Efficient Algorithm for Federated Learning Google ICML 2020 Video
FetchSGD: Communication-Efficient Federated Learning with Sketching UC Berkeley; Johns Hopkins University; Amazon ICML 2020 Video [Code]
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning EPFL; Google ICML 2020 Video
Federated Learning with Only Positive Labels Google ICML 2020 Video
From Local SGD to Local Fixed-Point Methods for Federated Learning Moscow Institute of Physics and Technology; KAUST ICML 2020 Slide Video
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization KAUST ICML 2020 Slide Video
Differentially-Private Federated Linear Bandits MIT NeurIPS 2020 [Code]
Federated Principal Component Analysis University of Cambridge; Quine Technologies NeurIPS 2020 [Code]
FedSplit: an algorithmic framework for fast federated optimization UC Berkeley NeurIPS 2020
Federated Bayesian Optimization via Thompson Sampling NUS; MIT NeurIPS 2020
Lower Bounds and Optimal Algorithms for Personalized Federated Learning KAUST NeurIPS 2020
Robust Federated Learning: The Case of Affine Distribution Shifts UC Santa Barbara; MIT NeurIPS 2020
An Efficient Framework for Clustered Federated Learning UC Berkeley; DeepMind NeurIPS 2020 [Code]
Distributionally Robust Federated Averaging 🔥 Pennsylvania State University NeurIPS 2020 [Code]
Personalized Federated Learning with Moreau Envelopes 🔥 The University of Sydney NeurIPS 2020 [Code]
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach MIT; UT Austin NeurIPS 2020
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge University of Southern California NeurIPS 2020 [Code] [Interpret(zh)]
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization CMU; Princeton University NeurIPS 2020
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning University of Wisconsin-Madison NeurIPS 2020
Federated Accelerated Stochastic Gradient Descent Stanford University NeurIPS 2020 [Code]
Inverting Gradients - How easy is it to break privacy in federated learning? 🔥 University of Siegen NeurIPS 2020 [Code]
Ensemble Distillation for Robust Model Fusion in Federated Learning EPFL NeurIPS 2020
Throughput-Optimal Topology Design for Cross-Silo Federated Learning INRIA NeurIPS 2020 [Code]
Bayesian Nonparametric Federated Learning of Neural Networks 🔥 IBM ICML 2019 [Code]
Analyzing Federated Learning through an Adversarial Lens 🔥 Princeton University; IBM ICML 2019 [Code]
Agnostic Federated Learning Google ICML 2019
cpSGD: Communication-efficient and differentially-private distributed SGD Princeton University; Google NeurIPS 2018
Federated Multi-Task Learning 🔥 Stanford; USC; CMU NeurIPS 2018 [Code]

FL in top DM conferences

In this section, we will summarize Federated Learning papers accepted by top DM(Data Mining) conference, Including KDD(ACM SIGKDD Conference on Knowledge Discovery and Data Mining).

Title Affiliation Venue Year TL;DR Materials
Collaboration Equilibrium in Federated Learning Department of Automation, Tsinghua University KDD 2022 CE4 [PDF] Code
Connected Low-Loss Subspace Learning for a Personalization in Federated Learning Ulsan National Institute of Science and Technology KDD 2022
FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks University of Virginia KDD 2022
Communication-Efficient Robust Federated Learning with Noisy Labels University of Pittsburgh KDD 2022 Comm-FedBiO5 [PDF]
FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency University of Science and Technology of China KDD 2022 FLDetector6 [PDF] Code
Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data HKUST KDD 2022 FedSVD 7 [PDF] Code
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy Shanghai Jiao Tong University KDD 2022 FedWalk1 [PDF]
EasyFGL: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning 🔥 Alibaba Group KDD 2022 FederatedScope-GNN 2 [PDF] Code
Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch Beihang University KDD 2022 Fed-LTD 8 [PDF]
[Interpret(zh)]
Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks USTC KDD 2022 [PDF]
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices Renmin University of China KDD 2022 InclusiveFL 9 [PDF]
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling Tsinghua University KDD 2022 FedAttack 10 [PDF]
Fed2: Feature-Aligned Federated Learning George Mason University; Microsoft; University of Maryland KDD 2021 PDF
FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data Nanjing University KDD 2021 Code
Federated Adversarial Debiasing for Fair and Trasnferable Representations Michigan State University KDD 2021 HomePage Code
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling University of Southern California KDD 2021 [Code] [Interpret(zh)]
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization Xidian University;JD Tech KDD 2021 PDF
FLOP: Federated Learning on Medical Datasets using Partial Networks Duke University KDD 2021 [Code]
FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems University College Dublin KDD 2020 video
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data JD Tech KDD 2020 PDF video

FL in top Secure conferences

In this section, we will summarize Federated Learning papers accepted by top Secure conferences, Including S&P(IEEE Symposium on Security and Privacy), CCS(Conference on Computer and Communications Security), USENIX Security(Usenix Security Symposium) and NDSS(Network and Distributed System Security Symposium).

Title Affiliation Venue Year TL;DR Materials
SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost Microsoft Research USENIX Security 2022 PUB. PDF code
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors University of Vermont USENIX Security 2022 PUB.
Label Inference Attacks Against Vertical Federated Learning Zhejiang University USENIX Security 2022 PUB. code
FLAME: Taming Backdoors in Federated Learning Technical University of Darmstadt USENIX Security 2022 PUB. PDF
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning University at Buffalo, SUNY NDSS 2022 PDF Unofficial code
Interpretable Federated Transformer Log Learning for Cloud Threat Forensics University of the Incarnate Word, TX, USA NDSS 2022 Unofficial code
FedCRI: Federated Mobile Cyber-Risk Intelligence Technical University of Darmstadt NDSS 2022
DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection Technical University of Darmstadt NDSS 2022 PDF
Private Hierarchical Clustering in Federated Networks National University Of Singapore CCS 2021
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping Duke University NDSS 2021 PDF code Video Slides
POSEIDON: Privacy-Preserving Federated Neural Network Learning Laboratory for Data Security, EPFL NDSS 2021 Video
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning University of Massachusetts Amherst NDSS 2021 code Video
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning The Ohio State University USENIX Security 2020 PDF code Video Slides
Poster: A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain University of Kansas CCS 2019
IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning Université du Québéc á Montréal S&P 2019
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning 🔥 University of Massachusetts Amherst S&P 2019 code

FL in top CV conferences

In this section, we will summarize Federated Learning papers accepted by top CV(computer vision) conference, Including CVPR(Computer Vision and Pattern Recognition), ICCV(IEEE International Conference on Computer Vision), ECCV(European Conference on Computer Vision), MM(ACM International Conference on Multimedia).

Title Affiliation Venue Year TL;DR Materials
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework Harbin Institute of Technology CVPR 2022 ATPFL 11 [PUB.]
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning Stanford University CVPR 2022 ViT-FL 12 [PUB.] [supp] [PDF] [Code]
FedCorr: Multi-Stage Federated Learning for Label Noise Correction Singapore University of Technology and Design CVPR 2022 FedCorr13 [PUB.] [supp] [PDF] [Code]
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning Duke University CVPR 2022 FedCor 14 [PUB.] [supp] [PDF]
Layer-Wised Model Aggregation for Personalized Federated Learning The Hong Kong Polytechnic University CVPR 2022 pFedLA 15 [PUB.] [supp] [PDF]
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning University of Central Florida CVPR 2022 FedAlign16 [PUB.] [supp] [PDF] [Code]
Federated Learning With Position-Aware Neurons Nanjing University CVPR 2022 PANs 17 [PUB.] [supp] [PDF]
RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning The Hong Kong University of Science and Technology CVPR 2022 RSCFed 18 [PUB.] [supp] [PDF] [Code]
Learn From Others and Be Yourself in Heterogeneous Federated Learning Wuhan University CVPR 2022 FCCL19 [PUB.] [code]
Robust Federated Learning With Noisy and Heterogeneous Clients Wuhan University CVPR 2022 RHFL 20 [PUB.] [supp] [Code]
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning Arizona State University CVPR 2022 ResSFL 21 [PUB.] [supp] [PDF] [Code]
FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction National University of Defense Technology CVPR 2022 FedDC 22 [PUB.] [supp] [PDF] [Code] [Interpret(zh)]
Federated Class-Incremental Learning Chinese Academy of Sciences; Northwestern University; University of Technology Sydney CVPR 2022 GLFC 23 [PUB.] [supp] [PDF] [Code]
Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning Peking University; JD Explore Academy; The University of Sydney CVPR 2022 FedFTG 24 [PUB.] [supp] [PDF]
Differentially Private Federated Learning With Local Regularization and Sparsification Chinese Academy of Sciences CVPR 2022 DP-FedAvg +BLUR + LUS 25 [PUB.] [supp] [PDF]
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage University of Tennessee; Oak Ridge National Laboratory; Google Research CVPR 2022 GGL 26 [PUB.] [supp] [PDF] [Code]
CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning Shanghai Jiao Tong University CVPR 2022 CD2-pFed 27 [PUB.] [PDF]
Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation Univ. of Pittsburgh; NVIDIA CVPR 2022 FedSM 28 [PUB.] [supp] [PDF]
Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning Johns Hopkins University CVPR 2021 FL-MRCM 29 [Code]
Model-Contrastive Federated Learning 🔥 National University of Singapore; UC Berkeley CVPR 2021 MOON 30 [pdf] [supp] [arXiv] [Code] [Interpret(zh)]
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space 🔥 The Chinese University of Hong Kong CVPR 2021 FedDG-ELCFS 31 [Code]
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective Duke University CVPR 2021 Soteria 32 [Code]
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment Peking University ICCV 2021 FedUFO 33
Ensemble Attention Distillation for Privacy-Preserving Federated Learning University at Buffalo ICCV 2021 FedAD 34 [PUB.]
Collaborative Unsupervised Visual Representation Learning from Decentralized Data Nanyang Technological University; SenseTime ICCV 2021 FedU 35 PDF
Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification Nanyang Technological University MM 2021 FedUReID 36 [PUB.] [PDF]
Federated Visual Classification with Real-World Data Distribution MIT; Google ECCV 2020 FedVC+FedIR 37 PDF Video
InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages MM 2020 InvisibleFL 38 [PUB.]
Performance Optimization of Federated Person Re-identification via Benchmark Analysis data. Nanyang Technological University MM 2020 FedReID 39 [PUB.] [PDF] Code [Interpret(zh)]

FL in top NLP conferences

In this section, we will summarize Federated Learning papers accepted by top AI and NLP conference, including ACL(Annual Meeting of the Association for Computational Linguistics), NAACL(North American Chapter of the Association for Computational Linguistics), EMNLP(Conference on Empirical Methods in Natural Language Processing) and COLING(International Conference on Computational Linguistics).

Title Affiliation Venue Year TL;DR Materials
Scaling Language Model Size in Cross-Device Federated Learning Google ACL workshop 2022 PDF
Intrinsic Gradient Compression for Scalable and Efficient Federated Learning Oxford, UK ACL workshop 2022 PDF
ActPerFL: Active Personalized Federated Learning Alexa AI, Amazon ACL workshop 2022 HomePage
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks 🔥 USC NAACL 2022 PDF Code
Federated Learning with Noisy User Feedback USC, Amazon NAACL 2022 PDF
Training Mixed-Domain Translation Models via Federated Learning Amazon NAACL 2022 HomePage PDF
Pretrained Models for Multilingual Federated Learning Johns Hopkins University NAACL 2022 PDF
Training Mixed-Domain Translation Models via Federated Learning Amazon NAACL 2022 HomePage PDF
Federated Chinese Word Segmentation with Global Character Associations University of Washington ACL workshop 2021 code
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation University of Science and Technology of China EMNLP 2021 PDF code
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories The Chinese University of Hong Kong (Shenzhen) EMNLP 2021 Video code
A Secure and Efficient Federated Learning Framework for NLP University of Connecticut EMNLP 2021 PDF
Distantly Supervised Relation Extraction in Federated Settings Institute of Automation, CAS EMNLP workshop 2021 PDF Code
Federated Learning with Noisy User Feedback USC, Amazon NAACL workshop 2021 PDF
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework Universität Hamburg, Germany NAACL workshop 2021
Understanding Unintended Memorization in Language Models Under Federated Learning Google NAACL workshop 2021 PDF
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction Institute of Automation, CAS EMNLP 2020
Empirical Studies of Institutional Federated Learning For Natural Language Processing Shenzhen, P.R.China EMNLP workshop 2020
Federated Learning for Spoken Language Understanding Peking University COLING 2020
Two-stage Federated Phenotyping and Patient Representation Learning Boston Children’s Hospital Harvard Medical School ACL workshop 2019 PDF

Framework

Federated Learning Framework

Platform Papers Affiliations Graph data and algorithms Tabular data and algorithms Materials
PySyft
Stars
 A generic framework for privacy preserving deep learning OpenMined Doc
FATE
Stars
 FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection WeBank ✅✅ Doc
Doc(zh)
MindSpore Federated
Stars
 HUAWEI Doc
Homepage
TFF(Tensorflow-Federated)
Stars
 Towards Federated Learning at Scale: System Design Google Doc
Homepage
FedML
Stars
 FedML: A Research Library and Benchmark for Federated Machine Learning FedML ✅✅ ✅ Doc
Flower
Stars
 Flower: A Friendly Federated Learning Research Framework flower.dev adap Doc
Fedlearner
Stars
 Bytedance
SecretFlow
Stars
 Ant group ✅ Doc
LEAF
Stars
 LEAF: A Benchmark for Federated Settings CMU
FederatedScope
Stars
 FederatedScope: A Flexible Federated Learning Platform for Heterogeneity Alibaba DAMO Academy ✅✅ Doc
Homepage
Rosetta
Stars
 matrixelements Doc
Homepage
PaddleFL
Stars
 Baidu Doc
OpenFL
Stars
 OpenFL: An open-source framework for Federated Learning Intel Doc
Privacy Meter
Stars
 Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning University of Massachusetts Amherst
IBM Federated Learning
Stars
 IBM Federated Learning: an Enterprise Framework White Paper IBM ✅ Papers
Fedlab
Stars
 FedLab: A Flexible Federated Learning Framework SMILELab Doc
Doc(zh)
Homepage
NVFlare
Stars
 NVIDIA Doc
FedScale
Stars
 FedScale: Benchmarking Model and System Performance of Federated Learning at Scale SymbioticLab(U-M)
FedJAX
Stars
 FEDJAX: Federated learning simulation with JAX Google
Xaynet
Stars
 XayNet HomePage Doc Whitepaper Legal Review
plato
Stars
 UofT
Galaxy Federated Learning
Stars
 GFL: A Decentralized Federated Learning Framework Based On Blockchain ZJU Doc
FLSim
Stars
 facebook research
PyVertical
Stars
 PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN OpenMined
FedTorch
Stars
 Distributionally Robust Federated Averaging Penn State
9nfl
Stars
 JD
FLUTE
Stars
 FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations microsoft Doc
Breaching
Stars
 A Framework for Attacks against Privacy in Federated Learning (papers)
FedLearn
Stars
 Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform JD
FEDn
Stars
 Scalable federated machine learning with FEDn scaleoutsystems Doc
EasyFL
Stars
 EasyFL: A Low-code Federated Learning Platform For Dummies NTU
FedTree
Stars
 Xtra Computing Group ✅✅ Doc
OpenFed
Stars
 OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework Doc
FedEval
Stars
 FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning HKU Doc
Flame
Stars
 Cisco
APPFL
Stars
 Doc
Clara NVIDIA

Datasets

(todo)

How to contact us

More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ (im.young@foxmail.com). Enjoy reading!

Acknowledgments

Many thanks ❤️ to the other awesome list:

Footnotes

  1. FedWalk, a random-walk-based unsupervised node embedding algorithm that operates in such a node-level visibility graph with raw graph information remaining locally. FedWalk，一个基于随机行走的无监督节点嵌入算法，在这样一个节点级可见度图中操作，原始图信息保留在本地。 ↩ ↩2

  2. FederatedScope-GNN present an easy-to-use FGL (federated graph learning) package. FederatedScope-GNN提出了一个易于使用的FGL（联邦图学习）软件包。 ↩ ↩2

  3. FGML a comprehensive review of the literature in Federated Graph Machine Learning. FGML 对图联邦机器学习的文献进行了全面回顾。 ↩

  4. CE propose the concept of benefit graph which describes how each client can benefit from collaborating with other clients and advance a Pareto optimization approach to identify the optimal collaborators. CE提出了利益图的概念，描述了每个客户如何从与其他客户的合作中获益，并提出了帕累托优化方法来确定最佳合作者。 ↩

  5. Comm-FedBiO propose a learning-based reweighting approach to mitigate the effect of noisy labels in FL. Comm-FedBiO提出了一种基于学习的重加权方法，以减轻FL中噪声标签的影响。 ↩

  6. FLDetector detects malicious clients via checking their model-updates consistency to defend against model poisoning attacks with a large number of malicious clients. FLDetector 通过检查其模型更新的一致性来检测恶意客户，以防御大量恶意客户的模型中毒攻击。 ↩

  7. FedSVD, a practical lossless federated SVD method over billion-scale data, which can simultaneously achieve lossless accuracy and high efficiency. FedSVD，是一种实用的亿级数据上的无损联合SVD方法，可以同时实现无损精度和高效率。 ↩

  8. Federated Learning-to-Dispatch (Fed-LTD), a framework that allows effective order dispatching by sharing both dispatching models and decisions while providing privacy protection of raw data and high efficiency. 解决跨平台叫车问题，即多平台在不共享数据的情况下协同进行订单分配。 ↩

  9. InclusiveFL is to assign models of different sizes to clients with different computing capabilities, bigger models for powerful clients and smaller ones for weak clients. InclusiveFL 将不同大小的模型分配给具有不同计算能力的客户，较大的模型用于强大的客户，较小的用于弱小的客户。 ↩

  10. FedAttack a simple yet effective and covert poisoning attack method on federated recommendation, core idea is using globally hardest samples to subvert model training. FedAttack是一种对联合推荐的简单而有效的隐蔽中毒攻击方法，核心**是利用全局最难的样本来颠覆模型训练。 ↩

  11. ATPFL helps users federate multi-source trajectory datasets to automatically design and train a powerful TP model. ATPFL帮助用户联合多源轨迹数据集，自动设计和训练强大的TP轨迹预测模型。 ↩

  12. ViT-FL demonstrate that self-attention-based architectures (e.g., Transformers) are more robust to distribution shifts and hence improve federated learning over heterogeneous data. ViT-FL证明了基于自注意力机制架构（如 Transformers）对分布的转变更加稳健，从而改善了异构数据的联邦学习。 ↩

  13. FedCorr, a general multi-stage framework to tackle heterogeneous label noise in FL, without making any assumptions on the noise models of local clients, while still maintaining client data privacy. FedCorr 一个通用的多阶段框架来处理FL中的异质标签噪声，不对本地客户的噪声模型做任何假设，同时仍然保持客户数据的隐私。 ↩

  14. FedCor, an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL. FedCor 一个建立在基于相关性的客户选择策略上的FL框架，以提高FL的收敛率。 ↩

  15. A novel pFL training framework dubbed Layer-wised Personalized Federated learning (pFedLA) that can discern the importance of each layer from different clients, and thus is able to optimize the personalized model aggregation for clients with heterogeneous data. "层级个性化联合学习"（pFedLA），它可以从不同的客户那里分辨出每一层的重要性，从而能够为拥有异质数据的客户优化个性化的模型聚合。 ↩

  16. FedAlign rethinks solutions to data heterogeneity in FL with a focus on local learning generality rather than proximal restriction. 我们重新思考FL中数据异质性的解决方案，重点是本地学习的通用性(generality)而不是近似限制。 ↩

  17. Position-Aware Neurons (PANs) , fusing position-related values (i.e., position encodings) into neuron outputs, making parameters across clients pre-aligned and facilitating coordinate-based parameter averaging. 位置感知神经元（PANs）将位置相关的值（即位置编码）融合到神经元输出中，使各客户的参数预先对齐，并促进基于坐标的参数平均化。 ↩

  18. Federated semi-supervised learning (FSSL) aims to derive a global model by training fully-labeled and fully-unlabeled clients or training partially labeled clients. RSCFed presents a Random Sampling Consensus Federated learning, by considering the uneven reliability among models from fully-labeled clients, fully-unlabeled clients or partially labeled clients. 联邦半监督学习（FSSL）旨在通过训练有监督和无监督的客户或半监督的客户来得出一个全局模型。 随机抽样共识联合学习，即RSCFed，考虑来自有监督的客户、无监督的客户或半监督的客户的模型之间不均匀的可靠性。 ↩

  19. FCCL (Federated Cross-Correlation and Continual Learning) For heterogeneity problem, FCCL leverages unlabeled public data for communication and construct cross-correlation matrix to learn a generalizable representation under domain shift. Meanwhile, for catastrophic forgetting, FCCL utilizes knowledge distillation in local updating, providing inter and intra domain information without leaking privacy. FCCL（联邦交叉相关和持续学习）对于异质性问题，FCCL利用未标记的公共数据进行交流，并构建交叉相关矩阵来学习领域转移下的可泛化表示。同时，对于灾难性遗忘，FCCL利用局部更新中的知识提炼，在不泄露隐私的情况下提供域间和域内信息。 ↩

  20. RHFL (Robust Heterogeneous Federated Learning) simultaneously handles the label noise and performs federated learning in a single framework. RHFL（稳健模型异构联邦学习），它同时处理标签噪声并在一个框架内执行联邦学习。 ↩

  21. ResSFL, a Split Federated Learning Framework that is designed to be MI-resistant during training. ResSFL一个分割学习的联邦学习框架，它被设计成在训练期间可以抵抗MI模型逆向攻击。 Model Inversion (MI) attack 模型逆向攻击 。 ↩

  22. FedDC propose a novel federated learning algorithm with local drift decoupling and correction. FedDC 一种带有本地漂移解耦和校正的新型联邦学习算法。 ↩

  23. Global-Local Forgetting Compensation (GLFC) model, to learn a global class incremental model for alleviating the catastrophic forgetting from both local and global perspectives. 全局-局部遗忘补偿（GLFC）模型，从局部和全局的角度学习一个全局类增量模型来缓解灾难性的遗忘问题。 ↩

  24. FedFTG, a data-free knowledge distillation method to fine-tune the global model in the server, which relieves the issue of direct model aggregation. FedFTG, 一种无数据的知识蒸馏方法来微调服务器中的全局模型，它缓解了直接模型聚合的问题。 ↩

  25. DP-FedAvg+BLUR+LUS study the cause of model performance degradation in federated learning under user-level DP guarantee and propose two techniques, Bounded Local Update Regularization and Local Update Sparsification, to increase model quality without sacrificing privacy. DP-FedAvg+BLUR+LUS 研究了在用户级DP保证下联合学习中模型性能下降的原因,提出了两种技术，即有界局部更新正则化和局部更新稀疏化，以提高模型质量而不牺牲隐私。 ↩

  26. Generative Gradient Leakage (GGL) validate that the private training data can still be leaked under certain defense settings with a new type of leakage. 生成梯度泄漏（GGL）验证了在某些防御设置下，私人训练数据仍可被泄漏。 ↩

  27. CD2-pFed, a novel Cyclic Distillation-guided Channel Decoupling framework, to personalize the global model in FL, under various settings of data heterogeneity. CD2-pFed，一个新的循环蒸馏引导的通道解耦框架，在各种数据异质性的设置下，在FL中实现全局模型的个性化。 ↩

  28. FedSM propose a novel training framework to avoid the client drift issue and successfully close the generalization gap compared with the centralized training for medical image segmentation tasks for the first time. 新的训练框架FedSM，以避免客户端漂移问题，并首次成功地缩小了与集中式训练相比在医学图像分割任务中的泛化差距。 ↩

  29. FL-MRCM propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy. FL-MRCM 一个基于联邦学习（FL）的解决方案，其中我们利用了不同机构的MR数据，同时保护了病人的隐私。 ↩

  30. MOON: model-contrastive federated learning. MOON is to utilize the similarity between model representations to correct the local training of individual parties, i.e., conducting contrastive learning in model-level. MOON 模型对比学习。MOON的关键**是利用模型表征之间的相似性来修正各方的局部训练，即在模型层面进行对比学习。 ↩

  31. FedDG-ELCFS A novel problem setting of federated domain generalization (FedDG), which aims to learn a federated model from multiple distributed source domains such that it can directly generalize to unseen target domains. Episodic Learning in Continuous Frequency Space (ELCFS), for this problem by enabling each client to exploit multi-source data distributions under the challenging constraint of data decentralization. FedDG-ELCFS 联邦域泛化（FedDG）旨在从多个分布式源域中学习一个联邦模型，使其能够直接泛化到未见过的目标域中。连续频率空间中的偶发学习（ELCFS），使每个客户能够在数据分散的挑战约束下利用多源数据分布。 ↩

  32. Soteria propose a defense against model inversion attack in FL, learning to perturb data representation such that the quality of the reconstructed data is severely degraded, while FL performance is maintained. Soteria 一种防御FL中模型反转攻击的方法,关键**是学习扰乱数据表示，使重建数据的质量严重下降，而FL性能保持不变。 ↩

  33. FedUFO a Unified Feature learning and Optimization objectives alignment method for non-IID FL. FedUFO 一种针对non IID FL的统一特征学习和优化目标对齐算法。 ↩

  34. FedAD propose a new distillation-based FL frame-work that can preserve privacy by design, while also consuming substantially less network communication resources when compared to the current methods. FedAD 一个新的基于蒸馏的FL框架，它可以通过设计来保护隐私，同时与目前的方法相比，消耗的网络通信资源也大大减少 ↩

  35. FedU a novel federated unsupervised learning framework. FedU 一个新颖的无监督联邦学习框架. ↩

  36. FedUReID, a federated unsupervised person ReID system to learn person ReID models without any labels while preserving privacy. FedUReID，一个联合的无监督人物识别系统，在没有任何标签的情况下学习人物识别模型，同时保护隐私。 ↩

  37. Introduce two new large-scale datasets for species and landmark classification, with realistic per-user data splits that simulate real-world edge learning scenarios. We also develop two new algorithms (FedVC, FedIR) that intelligently resample and reweight over the client pool, bringing large improvements in accuracy and stability in training. 为物种和地标分类引入了两个新的大规模数据集，每个用户的现实数据分割模拟了真实世界的边缘学习场景。我们还开发了两种新的算法（FedVC、FedIR），在客户池上智能地重新取样和重新加权，在训练中带来了准确性和稳定性的巨大改进 ↩

  38. InvisibleFL propose a privacy-preserving solution that avoids multimedia privacy leakages in federated learning. InvisibleFL 提出了一个保护隐私的解决方案，以避免联合学习中的多媒体隐私泄漏。 ↩

  39. FedReID implement federated learning to person re-identification and optimize its performance affected by statistical heterogeneity in the real-world scenario. FedReID 实现了对行人重识别任务的联邦学习，并优化了其在真实世界场景中受统计异质性影响的性能。 ↩