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arXiv Machine Learning By Miha O\v{z}bot, Seiichi Ozawa, Igor \v{S}krjanc

Evolving Federated Density Peak Clustering with Shuffler-Based Anonymization

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The paper introduces Evolving Federated Density Peak Clustering (eFDPC), a one‑shot federated clustering method that merges evolving fuzzy data‑stream clustering with density peak clustering. Each client independently updates micro‑clusters from private mini‑batches, and only selected density peaks are sent to the server, reducing communication. A shuffler‑based anonymization layer protects client contributions, and two variants—eFDPC‑P and eFDPC‑G—are evaluated on synthetic and real datasets, outperforming recent federated clustering baselines in one‑shot settings and handling high‑dimensional, non‑IID data.

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