Federated K-means Clustering

• URL: http://arxiv.org/abs/2310.01195v2
• Date: Fri, 16 Feb 2024 14:02:02 GMT
• Title: Federated K-means Clustering
• Authors: Swier Garst and Marcel Reinders
• Abstract summary: Federated learning is a technique that enables the use of distributed datasets for machine learning purposes without requiring data to be pooled. This work introduces an algorithm which implements K-means clustering in a federated manner.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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