mlsanityData to 7 Oct 2026

Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)

arXiv 2201.07316 ↗First posted 18 Jan 2022cs.LGcited by 29

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Abstract (arXiv)

Federated Learning (FL) is a setting where multiple parties with distributed data collaborate in training a joint Machine Learning (ML) model while keeping all data local at the parties. Federated clustering is an area of research within FL that is concerned with grouping together data that is globally similar while keeping all data local. We describe how this area of research can be of interest in itself, or how it helps addressing issues like non-independently-identically-distributed (i.i.d.) data in supervised FL frameworks. The focus of this work, however, is an extension of the federated fuzzy c-means algorithm to the FL setting (FFCM) as a contribution towards federated clustering. We propose two methods to calculate global cluster centers and evaluate their behaviour through challenging numerical experiments. We observe that one of the methods is able to identify good global clusters even in challenging scenarios, but also acknowledge that many challenges remain open.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 18 Jan 2022.

Builds on

15 of 33 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  4. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  5. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  6. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  7. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  8. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  9. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  10. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  11. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  12. 2019Practical Federated Gradient Boosting Decision Treescited by 53
3 more
  1. 2021Cluster-driven Graph Federated Learning over Multiple Domainscited by 43
  2. 2020Dynamic Clustering in Federated Learningcited by 27
  3. 2019A Federated Learning Approach for Mobile Packet Classificationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 10; 2024: 10; 2025: 4; 2026: 3.

29 papers.

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  1. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  2. 2023FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clusteringcited by 15
  3. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  4. 2024Asynchronous Federated Clustering with Unknown Number of Clusterscited by 11
  5. 2023Differentially Private Federated Clustering over Non-IID Datacited by 7
  6. 2022FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Datacited by 6
  7. 2023Federated Deep Multi-View Clustering with Global Self-Supervisioncited by 5
  8. 2023StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participationcited by 4
  9. 2022Federated clustering with GAN-based data synthesiscited by 3
  10. 2023Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologiescited by 3
  11. 2024CCFC: Bridging Federated Clustering and Contrastive Learningcited by 3
  12. 2024CCFC++: Enhancing Federated Clustering through Feature Decorrelationcited by 3
17 more
  1. 2024A Bayesian Framework for Clustered Federated Learningcited by 3
  2. 2023Federated K-Means Clustering via Dual Decomposition-based Distributed Optimizationcited by 2
  3. 2023Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated Learningcited by 2
  4. 2023Dynamically Weighted Federated k-Meanscited by 1
  5. 2024Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributionscited by 1
  6. 2024Jigsaw Game: Federated Clusteringcited by 1
  7. 2025Federated Variational Inference for Bayesian Mixture Modelscited by 1
  8. 2025FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learningcited by 1
  9. 2026A Federated Generalized Expectation-Maximization Algorithm for Mixture Models with an Unknown Number of Componentscited by 1
  10. 2023Federated Learning for Short Text Clusteringcited by 0
  11. 2024Architectural Blueprint For Heterogeneity-Resilient Federated Learningcited by 0
  12. 2024Federated One-Shot Ensemble Clusteringcited by 0
  13. 2024Federated Deep Subspace Clusteringcited by 0
  14. 2025OmniFC: Rethinking Federated Clustering via Lossless and Secure Distance Reconstructioncited by 0
  15. 2025LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mappingcited by 0
  16. 2026One-Shot Federated Clustering of Non-Independent Completely Distributed Datacited by 0
  17. 2026Federated Clustering with Unknown Local and Global Cluster Cardinalitiescited by 0