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 will continue to be collected and updated everything about federated learning materials, including research papers, conferences, blogs and beyond.
- Top Machine Learning conferences
- Top Computer Vision conferences
- Top Artificial Intelligence and Data Mining conferences
- Books
- Papers
- Talks and Tutorials
- Conferences and Workshops
- Blogs
- Open-Sources
In this section, we will summarize Federated Learning papers accepted by top machine learning conference, Including NeurIPS, ICML, ICLR.
In this section, we will summarize Federated Learning papers accepted by top computer vision conference, Including CVPR, ICCV, ECCV.
Conferences | Title | Affiliation | Slide & Code |
CVPR 2021 | Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning | Johns Hopkins University | code |
Model-Contrastive Federated Learning | National University of Singapore; UC Berkeley |
code | |
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space | The Chinese University of Hong Kong | code | |
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective | Duke University | code |
Conferences | Title | Affiliation | Slide & Code |
ECCV 2020 | Federated Visual Classification with Real-World Data Distribution | MIT; |
Video |
Conferences | Title | Affiliation | Slide & Code |
ICCV 2021 | Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment | Peking University | |
Ensemble Attention Distillation for Privacy-Preserving Federated Learning | University at Buffalo |
In this section, we will summarize Federated Learning papers accepted by top AI and DM conference, Including AAAI, AISTATS, KDD.
Conferences | Sessions | Title | Affiliation | Slide & Code |
KDD 2021 | Research Track | Fed2: Feature-Aligned Federated Learning | George Mason University; Microsoft; University of Maryland |
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FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data | Nanjing University | |||
Federated Adversarial Debiasing for Fair and Trasnferable Representations | Michigan State University | HomePage | ||
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling | University of Southern California | code | ||
Application Track | AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization | |||
FLOP: Federated Learning on Medical Datasets using Partial Networks | Duke University | code | ||
KDD 2020 | Research Track | FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems | University College Dublin | video |
Application Track | Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data | JD Tech | video |
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联邦学习(Federated Learning)
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联邦学习实战(Practicing Federated Learning)
Model Aggregation (or Model Fusion) refers to how to combine local models into a shared global model.
Title | Abbreviation | Conferences | Slide & Code |
Communication-Efficient Learning of Deep Networks from Decentralized Data | FedAvg | ASTATS 2017 | |
Bayesian Nonparametric Federated Learning of Neural Networks | PFNM | ICML 2019 | code |
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent | Krum | NeurIPS 2017 | |
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates | median; trimmed mean |
ICML 2018 | |
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms | median; mean |
NeurIPS 2020 | |
The hidden vulnerability of distributed learning in byzantium | Bulyan | ICML 2018 | |
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance | Zeno | ICML 2019 | code |
Statistical Model Aggregation via Parameter Matching | SPAHM | NeurIPS 2019 | code |
Fed+: A Unified Approach to Robust Personalized Federated Learning | Fed+ | ||
FEDERATED OPTIMIZATION IN HETEROGENEOUS NETWORKS | FedProx | MLSys 2020 | code |
Separation of Powers in Federated Learning | Truda |
Personalized federated learning refers to train a model for each client, based on the client’s own dataset and the datasets of other clients. There are two major motivations for personalized federated learning:
- Due to statistical heterogeneity across clients, a single global model would not be a good choice for all clients. Sometimes, the local models trained solely on their private data perform better than the global shared model.
- Different clients need models specifically customized to their own environment. As an example of model heterogeneity, consider the sentence: “I live in .....”. The next-word prediction task applied on this sentence needs to predict a different answer customized for each user. Different clients may assign different labels to the same data.
Personalized federated learning Survey paper:
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Three Approaches for Personalization with Applications to Federated Learning
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Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
Recommender system (RecSys) is widely used to solve information overload. In general, the more data RecSys use, the better the recommendation performance we can obtain.
Traditionally, RecSys requires the data that are distributed across multiple devices to be uploaded to the central database for model training. However, due to privacy and security concerns, such directly sharing user data strategies are no longer appropriate.
The incorporation of federated learning and RecSys is a promising approach, which can alleviate the risk of privacy leakage.
Methodology | Title | Conferences | Slide & Code |
Matrix Factorization | Secure federated matrix factorization | IEEE Intelligent Systems | |
Federated Multi-view Matrix Factorization for Personalized Recommendations | ECML-PKDD 2020 | video | |
Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning | ECML-PKDD 2019 | ||
Towards Privacy-preserving Mobile Applications with Federated Learning: The Case of Matrix Factorization | MobiSys 2019 | ||
Meta Matrix Factorization for Federated Rating Predictions | ACM SIGIR 2020 | code | |
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System | Arxiv | ||
GNN | FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation | Arxiv |
Methodology | Title | Conferences | Slide & Code |
Backdoor Attack | How To Backdoor Federated Learning | AISTATS 2020 | code |
Can You Really Backdoor Federated Learning? | Arxiv | ||
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning | NeurIPS 2020 | code | |
DBA: Distributed Backdoor Attacks against Federated Learning | ICLR 2020 | code |
Methodology | Title | Conferences | Slide & Code |
FL+DP | Federated Learning With Differential Privacy: Algorithms and Performance Analysis | IEEE Transactions on Information Forensics and Security | |
Differentially Private Federated Learning: A Client Level Perspective | Arxiv | code | |
Learning Differentially Private Recurrent Language Models | ICLR 2018 | ||
FL+HE | Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption | Arxiv | |
BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning | USENIX 2020 | code | |
FL+TEE | PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments | ACM MobiSys 2021 | |
Darknetz: towards model privacy at the edge using trusted execution environments. | ACM MobiSys 2020 | code video |
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A Little Is Enough: Circumventing Defenses For Distributed Learning | NeurIPS 2019 |
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RPN: A Residual Pooling Network for Efficient Federated Learning - ECAI 2020
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Federated Learning: Strategies for Improving Communication Efficiency - arXiv 2017
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Federated Learning with Matched Averaging - ICLR 2020
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One-Shot Federated Learning - arXiv 2019
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cpSGD: Communication-efficient and differentially-private distributed SGD - NeurIPS 2018
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Federated Optimization: Distributed Machine Learning for On-Device Intelligence - arXiv 2016
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Fair Resource Allocation in Federated Learning - arXiv 2019
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Agnostic Federated Learning - ICML 2019
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TensorFlow Federated (TFF): Machine Learning on Decentralized Data - Google, TF Dev Summit ‘19 2019
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Federated Learning: Machine Learning on Decentralized Data - Google, Google I/O 2019
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Federated Learning - Cloudera Fast Forward Labs, DataWorks Summit 2019
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GDPR, Data Shortage and AI - Qiang Yang, AAAI 2019 Invited Talk
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Making every phone smarter with Federated Learning - Google, 2018
- FL-ICML 2020 - Organized by IBM Watson Research.
- FL-IBM 2020 - Organized by IBM Watson Research and Webank.
- FL-NeurIPS 2019 - Organized by Google, Webank, NTU, CMU.
- FL-IJCAI 2019 - Organized by Webank.
- Google Federated Learning workshop - Organized by Google.
- What is Federated Learning - Nvidia 2019
- Online Federated Learning Comic - Google 2019
- Federated Learning: Collaborative Machine Learning without Centralized Training Data - Google AI Blog 2017
- Go Federated with OpenFL - Intel 2021