Federated k-Means over Networks
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908793807306752 |
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| author | Yang, Xu Rastelli, Salvatore Jung, Alexander |
| author_facet | Yang, Xu Rastelli, Salvatore Jung, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09718 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated k-Means over Networks Yang, Xu Rastelli, Salvatore Jung, Alexander Machine Learning 68T05, 68T10 I.5.3; I.2.11 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. |
| title | Federated k-Means over Networks |
| topic | Machine Learning 68T05, 68T10 I.5.3; I.2.11 |
| url | https://arxiv.org/abs/2510.09718 |