# Federated k-Means over Networks

arXiv: https://arxiv.org/abs/2510.09718 · HTML: https://mlsanity.com/paper/2510.09718/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [CoCoA: A General Framework for Communication-Efficient Distributed Optimization](https://mlsanity.com/paper/1611.02189/) (2016)
- [Network Lasso: Clustering and Optimization in Large Graphs](https://mlsanity.com/paper/1507.00280/) (2015)
- [Distributed k-Means and k-Median Clustering on General Topologies](https://mlsanity.com/paper/1306.0604/) (2013)
- [Federated Learning: From Theory to Practice](https://mlsanity.com/paper/2505.19183/) (2025)

## Built on by (most cited first, 1 of 1)

- [Federated Soft Clustering via Generalized Total Variation Minimization](https://mlsanity.com/paper/2609.19202/) (2026)

## Abstract (arXiv)

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.