Federated K-means Clustering

2023/10/02 by Swier Garst, Marcel Reinders, Garst, Swier +1 · 2 citations
Computer Science · Engineering · #Data Mining Algorithms and Applications #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #Privacy-Preserving Technologies in Data #Vehicular Ad Hoc Networks (VANETs) #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2310.01195

openalex publication_date 2023/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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• Insights into the Unknown: Federated Data Diversity Analysis on Molecular Data
• Distributed clustering in partially overlapping feature spaces

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