research-article

Long-Term Adaptive VCG Auction Mechanism for Sustainable Federated Learning With Periodical Client Shifting

Authors:

Yufeng ZhanAuthors Info & Claims

IEEE Transactions on Mobile Computing, Volume 23, Issue 5

Pages 6060 - 6073

https://doi.org/10.1109/TMC.2023.3317063

Published: 01 May 2024 Publication History

Abstract

Federated Learning (FL) system needs to incentivize clients since they may be reluctant to participate in the resource consuming process. Existing incentive mechanisms fail to construct a sustainable environment for the long-term development of FL system: 1) They seldom focus on system economic properties (e.g., social welfare, individual rationality, and incentive compatibility) to guarantee client attraction. 2) Current online auction modeling methods divide the whole continual process into multiple independent rounds and solve them one-by-one, which breaks the correlation between each round. Besides, the inherent characteristics of FL system (model-agnostic and privacy-sensitive) also prevent it from the optimal strategy by precise mathematical analysis. 3) Current system modelings ignore the practical problem of periodical client shifting, which cannot adaptively update its strategy to handle system dynamics. To overcome the above challenges, this paper proposes a long-term adaptive Vickrey–Clarke–Groves (VCG) auction mechanism for FL system, which incorporate a multi-branch deep reinforcement learning (DRL) algorithm. First, VCG auction is the only one that can simultaneously guarantee all crucial economic properties. Second, we extend the economic properties to long-term forms and apply the experience-driven DRL algorithm to directly obtain long-term optimal strategy, without any prior system knowledge. Third, we reconstruct a multi-branch DRL network to accommodate periodical client shifting by adaptive decision head switching for different time periods. Finally, we theoretically prove he extended economic properties (i.e., IC) and conduct extensive experiments on several real-world datasets. Compared with state-of-the-art approaches, the long-term social welfare of FL system increases by 36% with a 37% reduction in payment. Besides, the multi-branch network can adaptively handle periodical client shifting on the timeline.

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Cited By

Wan TFeng SLiao WJiang NZhou J(2024)A Secure and Fair Client Selection Based on DDPG for Federated LearningInternational Journal of Intelligent Systems10.1155/2024/23140192024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/2314019
Huang TTan STang QXie RZhang CYu F(2024)Coordinating Services and Networks With NaaS Tickets Towards Service Customization in Distributed CloudsIEEE Transactions on Mobile Computing10.1109/TMC.2024.338681123:12(10984-10999)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TMC.2024.3386811

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cover image IEEE Transactions on Mobile Computing

IEEE Transactions on Mobile Computing Volume 23, Issue 5

May 2024

2994 pages

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1536-1233 © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 May 2024

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Cited By

Wan TFeng SLiao WJiang NZhou J(2024)A Secure and Fair Client Selection Based on DDPG for Federated LearningInternational Journal of Intelligent Systems10.1155/2024/23140192024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/2314019
Huang TTan STang QXie RZhang CYu F(2024)Coordinating Services and Networks With NaaS Tickets Towards Service Customization in Distributed CloudsIEEE Transactions on Mobile Computing10.1109/TMC.2024.338681123:12(10984-10999)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TMC.2024.3386811

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