Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers

• URL: http://arxiv.org/abs/2603.12684v1
• Date: Fri, 13 Mar 2026 05:58:35 GMT
• Title: Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers
• Abstract summary: Federated Clustering (FC) is an emerging and promising solution in exploring data distribution patterns from distributed and privacy-protected data in an unsupervised manner.<n>We propose a novel FC framework named Fed-$k*$-HC, which can automatically determine an optimal number of clusters $k*$ based on the data distribution explored through hierarchical clustering.
• Score: 13.396894845465468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Clustering (FC) is an emerging and promising solution in exploring data distribution patterns from distributed and privacy-protected data in an unsupervised manner. Existing FC methods implicitly rely on the assumption that clients are with a known number of uniformly sized clusters. However, the true number of clusters is typically unknown, and cluster sizes are naturally imbalanced in real scenarios. Furthermore, the privacy-preserving transmission constraints in federated learning inevitably reduce usable information, making the development of robust and accurate FC extremely challenging. Accordingly, we propose a novel FC framework named Fed-$k^*$-HC, which can automatically determine an optimal number of clusters $k^*$ based on the data distribution explored through hierarchical clustering. To obtain the global data distribution for $k^*$ determination, we let each client generate micro-subclusters. Their prototypes are then uploaded to the server for hierarchical merging. The density-based merging design allows exploring clusters of varying sizes and shapes, and the progressive merging process can self-terminate according to the neighboring relationships among the prototypes to determine $k^*$. Extensive experiments on diverse datasets demonstrate the FC capability of the proposed Fed-$k^*$-HC in accurately exploring a proper number of clusters.

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