Resource Allocation for Federated Learning with Heterogeneous Computing Capability in Cloud–Edge–Client IoT Architecture

A federated learning (FL) framework for cloud–edge–client collaboration performs local aggregation of model parameters through edges, reducing communication overhead from clients to the cloud. This framework is particularly suitable for Internet of Things (IoT)-based secure computing scenarios that...

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Egile Nagusiak:	Xubo Zhang, Yang Luo
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	MDPI AG 2025-05-01
Saila:	Future Internet
Gaiak:	federated learning heterogeneous clients model dropout
Sarrera elektronikoa:	https://www.mdpi.com/1999-5903/17/6/243
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