Evolving federated density peak clustering with shuffler-based anonymization

Ožbot, Miha (avtor), Ozawa, Seiichi (avtor), Škrjanc, Igor (avtor)

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-P , as well as eFDPC-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.

• Jezik: Angleški jezik
• Ključne besede: federated learning, streaming clustering, non-IID data, evolving fuzzy systems, unsupervised learning, density peak clustering
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 19 str.
• Številčenje: Vol. 17, issue 4, art. 123
• PID: 20.500.12556/RUL-189713
• UDK: 681.5
• ISSN pri članku: 1868-6486
• DOI: 10.1007/s12530-026-09884-0
• COBISS.SI-ID: 300697859
• Datum objave v RUL: 13.10.2026

Gradivo je del revije

• Naslov: Evolving systems
• Skrajšan naslov: Evol. syst.
• Založnik: Springer
• ISSN: 1868-6486
• COBISS.SI-ID: 8148820

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: federativno učenje, gručenje podatkovnih tokov, heterogeno porazdeljeni podatki (non-IID), razvijajoči se mehki sistemi, nenadzorovano učenje, gručenje po vrhovih gostote

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0219
• Naslov: Modeliranje, simulacija in vodenje procesov