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Incentive mechanism design for semi-asynchronous blockchain-based federated edge learning
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Authors: Xuanzhang Liu, Jiyao Liu, Xinliang Wei, Yu Wang
Status: Final
Date of publication: 25 June 2025
Published in: ITU Journal on Future and Evolving Technologies, Volume 6 (2025), Issue 2, Pages 119-131
Article DOI : https://doi.org/10.52953/XFJH6676
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Abstract:
Federated learning at edge systems not only mitigates privacy concerns by keeping data localized but also leverages edge computing resources to enable real-time AI inference and decision-making. In a blockchain-based federated learning framework over edge clouds, edge servers as clients can contribute private data or computing resources to the overall training or mining task for secure model aggregation. To overcome the impractical assumption that edge servers will voluntarily join training or mining, it is crucial to design an incentive mechanism that motivates edge servers to achieve optimal training and mining outcomes. In this paper, we investigate the incentive mechanism design for a semi-asynchronous blockchain-based federated edge learning system. We model the resource pricing mechanism among edge servers and task publishers as a Stackelberg game and prove the existence and uniqueness of a Nash equilibrium in such a game. We then propose an iterative algorithm based on the Alternating Direction Method of Multipliers (ADMM) to achieve the optimal strategies for each participating edge server. Finally, our simulation results verify the convergence and efficiency of our proposed scheme. |
Keywords: Blockchain, edge AI, edge computing, federated learning, incentive mechanism, semi-asynchronous, Stackelberg game
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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