Personalized lightweight federated learning for efficient and private model training in heterogeneous data environments

Personalized federated learning (PFL) enables collaborative model training across devices while adapting to heterogeneous data, but faces resource constraints on edge devices. Combining PFL with pruning techniques helps address these constraints. A challenge is that one-size-fits-all pruning strateg...

Täydet tiedot

Bibliografiset tiedot

Päätekijä:	Ying Wang
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Elsevier 2025-12-01
Sarja:	Systems and Soft Computing
Aiheet:	Personalized lightweight federated learning Pruning Hy-pernetwork Model training Heterogeneous data
Linkit:	http://www.sciencedirect.com/science/article/pii/S2772941925000304
Tagit:	Lisää tagi Ei tageja, Lisää ensimmäinen tagi!