Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment

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To overcome the three key challenges of federated learning in heterogeneous edge computing, i.e., edge heterogeneity, data Non-IID, and communication resource constraints, a grouping asynchronous federated learning (FedGA) mechanism was proposed.Edge nodes were divided into multiple groups, each of...

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Bibliographic Details
Published in:	Tongxin xuebao
Main Authors:	Qianpiao MA, Qingmin JIA, Jianchun LIU, Hongli XU, Renchao XIE, Tao HUANG
Format:	Article
Language:	Chinese
Published:	Editorial Department of Journal on Communications 2023-11-01
Subjects:	edge computing federated learning Non-IID heterogeneity convergence analysis
Online Access:	http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023196/

Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment by Qianpiao MA, Qingmin JIA, Jianchun LIU, Hongli XU, Renchao XIE, Tao HUANG

Published in Tongxin xuebao (2023-11-01)

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Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment

Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment by Qianpiao MA, Qingmin JIA, Jianchun LIU, Hongli XU, Renchao XIE, Tao HUANG

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