Clustered Hierarchical Distributed Federated Learning

Gou, Y., Wang, R., Zongyao, L., Imran, M. A. and Zhang, L. (2022) Clustered Hierarchical Distributed Federated Learning. In: ICC 2022 - IEEE International Conference on Communications, Seoul, South Korea, 16-20 May 2022, pp. 177-182. ISBN 9781538683477 (doi: 10.1109/ICC45855.2022.9838880)

Abstract

In recent years, due to the increasing concern about data privacy security, federated learning, whose clients only synchronize the model rather than the personal data, has developed rapidly. However, the traditional federated learning system still has a high dependence on the central server, an unguaranteed enthusiasm of clients and reliability of the central server, and extremely high consumption of communication resources. Therefore, we propose Clustered Hierarchical Distributed Federated Learning to solve the above problems. We motivate the participation of clients by clustering and solve the dependence on the central server through distributed architecture. We apply a hierarchical segmented gossip protocol and feedback mechanism for in-cluster model exchange and gossip protocol for communication between clusters to make full use of bandwidth and have good training convergence. Experimental results demonstrate that our method has better performance with less communication resource consumption.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhang, Professor Lei and Wang, Ruiyu and Imran, Professor Muhammad and Gou, Yan and Li, Zongyao
• Authors: Gou, Y., Wang, R., Zongyao, L., Imran, M. A., and Zhang, L.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• ISSN: 1938-1883
• ISBN: 9781538683477
• Copyright Holders: Copyright © 2022 IEEE
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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