Federated K-means Clustering

Swier Garst, Marcel Reinders
10/2/2023
cs.LGcs.DC

No official code found. Best implementation: jonnyascott/fed-dp-kmeans (6 stars, Apache-2.0, last commit 2025-07-02).

Abstract

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.

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Code Implementations(1)

jonnyascott/fed-dp-kmeansCommunity54%
Apache-2.0

Official Implementation for "Differentially Private Federated k-Means Clustering with Server-Side Data" [ICML 2025]

60Shell, PythonJun 2, 20251 years agoApache-2.0

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Cite this paper

@article{garst2023federated,
  title  = {Federated K-means Clustering},
  author = {Swier Garst and Marcel Reinders},
  year   = {2023},
  eprint = {2310.01195},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.01195v2}
}

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