Label-free dataset profiling for federated client clustering

Radovič, Boris (avtor), Canini, Marco (avtor), Pejović, Veljko (avtor)

Clustering clients into groups with relatively homogeneous data distributions is a key strategy for improving federated learning under non-independent and identically distributed data. However, most state-of-the-art clustering approaches require clients to possess labeled datasets and perform substantial local computation, limiting their applicability in real-world settings. To address these limitations, we introduce CoLEDS, a method for profiling unlabeled client datasets with minimal computational overhead. CoLEDS trains a model using a contrastive learning objective defined across multiple clients and optimized in a distributed fashion through joint client–server coordination. The resulting model embeds key properties of client datasets into low-dimensional vectors that are shared with the server for clustering. Extensive empirical evaluation shows that these profiles accurately capture latent dataset characteristics. By clustering clients based on these representations, CoLEDS yields federatively trained models that are better aligned with individual data distributions and enables appropriate model assignment even for clients that do not participate in federated training

• Jezik: Angleški jezik
• Ključne besede: federated learning, dataset profiling, data non-IIDness, client clustering
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 36 str.
• Številčenje: Vol. 40, iss. 5, art. 72
• PID: 20.500.12556/RUL-185008
• UDK: 004.85:004.75
• ISSN pri članku: 1384-5810
• DOI: 10.1007/s10618-026-01240-9
• COBISS.SI-ID: 285152771
• Datum objave v RUL: 20.07.2026

Gradivo je del revije

• Naslov: Data mining and knowledge discovery
• Založnik: Springer Nature
• ISSN: 1384-5810
• COBISS.SI-ID: 14278183

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: zvezno učenje, profiliranje podatkovne množice, gručenje klientov

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: J2-3047-2021
• Naslov: Kontekstno-odvisno približno računanje na mobilnih napravah

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: N2-0393-2025
• Naslov: Približno računanje za prilagodljivo porazdeljeno umetno inteligenco