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Evolving federated density peak clustering with shuffler-based anonymization

Miha Ožbot, Seiichi Ozawa, Igor Škrjanc · 2026 · Evolving Systems

DOI: 10.1007/s12530-026-09884-0 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

クライアントの最新のサーバーバージョンでは、この論文では、変異的なクラスタリングとシュフラーベースの匿名化に基づく新しいデータワンショット連結クラスタリングアプローチを紹介します。連結型クラスタリングでは、各ラウンドはサーバーに追加のモデル情報を明らかにし、ローカルデータセットの特性を推定する機会を増加させることができ、サウンドサンプルが決して共有されない場合でも、結果として、クライアントとサーバーのコミュニケーションの単一ラウンドが少なくなり、無リンク性が重要であるため、送信された情報は単一のクライアントに割り当てられない。データストリームクラスタリングアルゴリズムは、データを単一のパスで学

Machine translation (Meta M2M100); the English original follows.

Abstract

Abstract In this paper, we present a novel one-shot federated clustering approach based on evolving fuzzy clustering and shuffler-based anonymization. In federated clustering, each round reveals additional model information to the server and can increase the opportunity to infer properties of local datasets, even when raw samples are never shared. Consequently, fewer client–server exchanges and unlinkability are important, so transmitted information cannot be attributed to any single client. Data-stream clustering algorithms, which learn in a single pass over the data, align well with the one-shot federated learning paradigm, where a single communication round can be sufficient. The proposed Evolving Federated Density Peak Clustering (eFDPC) method combines evolving fuzzy data-stream clustering and density peak clustering in a federated setting. Each client independently evolves micro-clusters from private data mini-batches through cluster addition and merging. To limit communication, density peak clustering is applied to the learned micro-clusters and only the selected peaks are transmitted, rather than the full local models. A shuffler-based layer is introduced between the clients and the server to anonymize contributions prior to reconstruction and global peak selection. We consider a peak-only variant, eFDPC- $$\mathcal {P}$$ , as well as eFDPC- $$\mathcal {G}$$ with server-side label propagation. Experiments on synthetic and real-world benchmark datasets show that eFDPC outperforms recent federated clustering baselines in one-shot settings and scales to higher-dimensional, non-IID data.

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Topics: Cluster analysis, Computer science, Benchmark (surveying), Data mining, Fuzzy clustering, Cluster (spacecraft)

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