mlsanityData to 7 Oct 2026

Federated K-means Clustering

arXiv 2310.01195 ↗First posted 2 Oct 2023cs.LG cs.DCcited by 9

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Abstract (arXiv)

Federated learning is a technique that enables the use of distributed datasets for machine learning purposes without requiring data to be pooled, thereby better preserving privacy and ownership of the data. While supervised FL research has grown substantially over the last years, unsupervised FL methods remain scarce. This work introduces an algorithm which implements K-means clustering in a federated manner, addressing the challenges of varying number of clusters between centers, as well as convergence on less separable datasets.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 2 Oct 2023.

Builds on

6 of 16 references.

References · 6

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2018LEAF: A Benchmark for Federated Settingscited by 780
  4. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  5. 2017EMNIST: an extension of MNIST to handwritten letterscited by 212
  6. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 5; 2026: 3.

9 papers.

  1. 2025Representation learning to advance multi-institutional studies with electronic health record data from US and Francecited by 5
  2. 2023Dynamically Weighted Federated k-Meanscited by 1
  3. 2025Federated Variational Inference for Bayesian Mixture Modelscited by 1
  4. 2025Distributed clustering in partially overlapping feature spacescited by 0
  5. 2025Insights into the Unknown: Federated Data Diversity Analysis on Molecular Datacited by 0
  6. 2025Federated Multi-Task Clusteringcited by 0
  7. 2026A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbinescited by 0
  8. 2026Variational Consensus Monte Carlo for Bayesian Mixturecited by 0
  9. 2026Federated Clustering with Unknown Local and Global Cluster Cardinalitiescited by 0