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arXiv · 2510.09718

Federated k-Means over Networks

Xu Yang·Salvatore Rastelli·Alexander Jung
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Abstract

We study federated clustering, where interconnected devices collaboratively cluster the data points of private local datasets. Focusing on hard clustering via the k-means principle, we formulate federated k-means as an instance of generalized total variation minimization (GTVMin). This leads to a federated k-means algorithm in which each device updates its local cluster centroids by solving a regularized k-means problem with a regularizer that enforces consistency between neighbouring devices. The resulting algorithm is privacy-friendly, as only aggregated information is exchanged.

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Xu Yang, Salvatore Rastelli, Alexander Jung. 2025-10-10. Federated k-Means over Networks. https://arxiv.org/abs/2510.09718

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