- Federated Learning Market
Advances and Open Problems in Federated Learning, https://arxiv.org/abs/1912.04977
A Field Guide to Federated Optimization, https://arxiv.org/abs/2107.06917
Personalized FL
동적인 디바이스 환경에서 적응적 연합학습기술 개발, https://github.com/Kwangkee/FL/blob/main/AFL.md
FL@CarnegieMellon, https://github.com/Kwangkee/FL/blob/main/[email protected]
FL@Meta, https://github.com/Kwangkee/FL/blob/main/[email protected]
Benchmark for PFL, https://github.com/Kwangkee/FL/blob/main/Benchmark4PFL.md
Blockchain-enabled FL
FL@Nanyang, https://github.com/Kwangkee/FL/blob/main/[email protected]
FL@CSIRO, https://github.com/Kwangkee/FL/blob/main/[email protected]
BCFL@Korea, https://github.com/Kwangkee/FL/blob/main/[email protected]
BCFL@Equideum, https://github.com/Kwangkee/FL/blob/main/[email protected]
FL@MS, https://github.com/Kwangkee/FL/blob/main/[email protected]
FL@Apple, https://github.com/Kwangkee/FL/blob/main/[email protected]
FL@ClientSelection, https://github.com/Kwangkee/FL/blob/main/[email protected]
FL@Incentive, https://github.com/Kwangkee/FL/blob/main/[email protected]
FL@Medical, https://github.com/Kwangkee/FL/blob/main/[email protected]
https://github.com/innovation-cat/Awesome-Federated-Machine-Learning
■ BCFL Survey, 블록체인 융합 연합학습 (Blockchain-enabled Federated Learning)
- Blockchain-based Federated Learning for Securing Internet of Things: A Comprehensive Survey, https://dl.acm.org/doi/10.1145/3560816
- Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey, https://link.springer.com/article/10.1007/s00500-021-06496-5
- Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges, https://ieeexplore.ieee.org/abstract/document/9403374
- Blockchain-based Federated Learning: A Comprehensive Survey, https://arxiv.org/abs/2110.02182
- Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges, https://www.sciencedirect.com/science/article/pii/S0167404821001796
- Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey, https://link.springer.com/article/10.1007/s00500-021-06496-5
■ BCFL Implementation
- 2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments, https://arxiv.org/abs/2011.07516, Code: https://github.com/cai-harry/2CP
- BCFL@JPMorgan, Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach, https://arxiv.org/abs/2107.10243
■ 보상/인센티브 제공 연합학습 (Incentivized Federated Learning)
■ 공정한 연합학습 (Fair Federated Learning)
- GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning, https://pubsonline.informs.org/doi/full/10.1287/ijds.2022.0022
- Improving Fairness via Federated Learning, https://arxiv.org/abs/2110.15545?context=cs
- Fair and Robust Federated Learning Through Personalization, http://proceedings.mlr.press/v139/li21h/li21h.pdf
- KDD 2021 Tutorial on Towards Fair Federated Learning, www.cas.mcmaster.ca/~chul9/Contents/KDD_2021_Tutorial.html
■ 신뢰할 수 있는 연합학습 (Trustworthy Federated Learning)
FL-IJCAI 2022 : International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI'22), http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=156561©ownerid=80806
Towards Trustworthy AI: Blockchain-based Architecture Design for Accountability and Fairness of Federated Learning Systems, https://ieeexplore.ieee.org/abstract/document/9686048
TrustFed: A Framework for Fair and Trustworthy Cross-Device Federated Learning in IIoT, https://ieeexplore.ieee.org/abstract/document/9416805
■ 설명 가능한 연합학습 (eXplainable Federated Learning)
EVFL: An explainable vertical federated learning for data-oriented Artificial Intelligence systems, https://www.sciencedirect.com/science/article/abs/pii/S1383762122000583
Interpret Federated Learning with Shapley Values, https://arxiv.org/pdf/1905.04519
Explainable Federated Learning for Taxi Travel Time Prediction, www.scitepress.org/Papers/2021/104856/104856.pdf
■ 통계적/시스템적 이질성에 강인한 적응적 연합학습 (Adaptive/Personalized Federated Learning)
Federated Meta-Learning with Fast Convergence and Efficient Communication, https://arxiv.org/abs/1802.07876
Improving Federated Learning Personalization via Model Agnostic Meta Learning, https://arxiv.org/abs/1909.12488
Personalized Federated Learning: A Meta-Learning Approach, https://arxiv.org/abs/2002.07948
Towards personalized federated learning, https://scholar.google.com/citations?view_op=view_citation&hl=ko&user=eXgoTXMAAAAJ&sortby=pubdate&citation_for_view=eXgoTXMAAAAJ:0d9pApVQ-n0C