mlsanityData to 14 Oct 2026

A Bayesian Framework for Clustered Federated Learning

arXiv 2410.15473 ↗First posted 20 Oct 2024cs.LG cs.DC stat.MLcited by 3

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Abstract (arXiv)

One of the main challenges of federated learning (FL) is handling non-independent and identically distributed (non-IID) client data, which may occur in practice due to unbalanced datasets and use of different data sources across clients. Knowledge sharing and model personalization are key strategies for addressing this issue. Clustered federated learning is a class of FL methods that groups clients that observe similarly distributed data into clusters, such that every client is typically associated with one data distribution and participates in training a model for that distribution along their cluster peers. In this paper, we present a unified Bayesian framework for clustered FL which associates clients to clusters. Then we propose several practical algorithms to handle the, otherwise growing, data associations in a way that trades off performance and computational complexity. This work provides insights on client-cluster associations and enables client knowledge sharing in new ways. The proposed framework circumvents the need for unique client-cluster associations, which is seen to increase the performance of the resulting models in a variety of experiments.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 34 of its 71 references that are in the map. First posted on arXiv 20 Oct 2024.

Builds on

34 of 71 references.

References · 34

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  9. 2021Towards Personalized Federated Learningcited by 464
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22 more
  1. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 312
  2. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  3. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 269
  4. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  5. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  6. 2019Federated Evaluation of On-device Personalizationcited by 147
  7. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
  8. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 124
  9. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
  10. 2019Variational Federated Multi-Task Learningcited by 69
  11. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  12. 2021FedSoft: Soft Clustered Federated Learning with Proximal Local Updatingcited by 46
  13. 2021A Bayesian Federated Learning Framework with Online Laplace Approximationcited by 41
  14. 2020Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descentcited by 35
  15. 2022On the Convergence of Clustered Federated Learningcited by 31
  16. 2022Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)cited by 30
  17. 2022FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distributioncited by 22
  18. 2023Bayesian Federated Learning: A Surveycited by 22
  19. 2023Federated Learning for Computer Visioncited by 22
  20. 2021Personalized Federated Learning over non-IID Data for Indoor Localizationcited by 6
  21. 2022Bayesian data fusion with shared priorscited by 3
  22. 2023Jammer classification with Federated Learningcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clusteringcited by 0
  2. 2026FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inferencecited by 0
  3. 2026FedLNS: Leverage LayerNorm Signature Modeling to Mitigate Adversarial Manipulation in Federated LLMscited by 0