Incentive-driven Federated Learning in Mobile Edge Networks

Yanlang Zheng, Huan Zhou, Liang Zhao, Shouzhi Xu, Victor C.M. Leung

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Federated Learning (FL) is proposed as a privacy-preserving distributed learning methodology that can better protect the privacy and reduce communication costs. To stimulate sufficient User Equipments (UEs) to participate in FL, proper incentives need to be designed for FL. Existing incentive mechanisms do not jointly consider UE selection and local learning accuracy optimization to reduce the training expenditure. This paper designs a reverse auction-based incentive mechanism for FL to minimize the training expenditure of Base Station (BS). To this end, we first propose a Greedy Winner Determination (GWD) algorithm to select UEs with the minimum bidding prices. Then, we incorporate the Particle Swarm Optimization (PSO)-based local learning accuracy optimization into UE selection to further reduce the training expenditure of BS. In addition, we design a Vickrey Clarke Groves (VCG)-based payment rule to determine the payment to each participating UE. The simulation experiments show that our proposed PSO with Winner Determination (PSOWD) algorithm is superior to other existing methods in different scenarios.

源语言英语
主期刊名Proceedings - 2023 IEEE 43rd International Conference on Distributed Computing Systems Workshops, ICDCSW 2023
出版商Institute of Electrical and Electronics Engineers Inc.
1-6
页数6
ISBN(电子版)9798350328127
DOI
出版状态已出版 - 2023
已对外发布
活动43rd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2023 - Hong Kong, 中国
期限: 18 7月 202321 7月 2023

出版系列

姓名Proceedings - 2023 IEEE 43rd International Conference on Distributed Computing Systems Workshops, ICDCSW 2023

会议

会议43rd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2023
国家/地区中国
Hong Kong
时期18/07/2321/07/23

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