Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare
Not on the map. Why?
Abstract (arXiv)
In this paper, we address the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel algorithm, which we term Cross-silo Robust Clustered Federated Learning (CS-RCFL). Our approach leverages the Wasserstein distance to construct ambiguity sets around each client's empirical distribution that capture possible distribution shifts in the local data, enabling evaluation of worst-case model performance. We then propose a model-agnostic integer fractional program to determine the optimal distributionally robust clustering of clients into coalitions so that possible biases in the local models caused by statistically heterogeneous client datasets are avoided, and analyze our method for linear and logistic regression models. Finally, we discuss a federated learning protocol that ensures the privacy of client distributions, a critical consideration, for instance, when clients are healthcare institutions. We evaluate our algorithm on synthetic and real-world healthcare data.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 38 references that are in the map. First posted on arXiv 9 Oct 2024.
Builds on
26 of 38 references.
References · 26
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2019Advances and Open Problems in Federated Learningcited by 3,046
- 2019Towards Federated Learning at Scale: System Designcited by 832
- 2020An Efficient Framework for Clustered Federated Learningcited by 511
- 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
- 2021Towards Personalized Federated Learningcited by 464
- 2019Agnostic Federated Learningcited by 456
- 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
- 2015Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulationscited by 291
- 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
- 2021Federated Learning for Smart Healthcare: A Surveycited by 184
- 2015Distributionally Robust Logistic Regressioncited by 126
14 more
- 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
- 2021Distributionally Robust Federated Averagingcited by 104
- 2020Robust Federated Learning: The Case of Affine Distribution Shiftscited by 102
- 2021Flexible Clustered Federated Learning for Client-Level Data Distribution Shiftcited by 47
- 2022Cross-Silo Federated Learning: Challenges and Opportunitiescited by 38
- 2022On the Convergence of Clustered Federated Learningcited by 31
- 2022Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptationcited by 25
- 2021Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfercited by 23
- 2022FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distributioncited by 22
- 2020Device Heterogeneity in Federated Learning: A Superquantile Approachcited by 21
- 2023Optimizing the Collaboration Structure in Cross-Silo Federated Learningcited by 19
- 2023FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clusteringcited by 15
- 2023Provably Personalized and Robust Federated Learningcited by 11
- 2022On the Generalization of Wasserstein Robust Federated Learningcited by 9
Built on by
Citing papers per year: 2025: 2.
2 papers.