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

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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Bibliographic Details
Main Authors: Xubo Zhang, Yang Luo
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Future Internet
Subjects:
federated learning
heterogeneous clients
model dropout
Online Access:https://www.mdpi.com/1999-5903/17/6/243
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https://www.mdpi.com/1999-5903/17/6/243

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