ATC-FL: Adaptive Thompson Sampling for Efficient Clustering in Federated Learning

Federated Learning (FL) enables collaborative model training across decentralized clients without sharing raw data. To improve scalability and address client heterogeneity, clustering-based FL methods group similar clients to train specialized models. However, under realistic network constraints, ex...

Descripció completa

Dades bibliogràfiques

Autors principals:	Shivani Sanjay Kolekar, Jisoo Shin, David J. Richter, Kyungbaek Kim
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2026-01-01
Col·lecció:	IEEE Access
Matèries:	Federated learning client clustering multi-armed bandits Thompson sampling distributed optimization
Accés en línia:	https://ieeexplore.ieee.org/document/11450082/
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!