Hybrid Blockchain-Based Resource Trading System for Federated Learning in Edge Computing

【Author】 Fan, Sizheng; Zhang, Hongbo; Zeng, Yuchen; Cai, Wei

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】By training a machine learning algorithm across multiple decentralized edge nodes, federated learning (FL) ensures the privacy of the data generated by the massive Internet-of-Things (IoT) devices. To economically encourage the participation of heterogeneous edge nodes, a transparent and decentralized trading platform is needed to establish a fair market among distinct edge companies. In this article, we propose a hybrid blockchain-based resource trading system that combines the advantages of both public and consortium blockchains. We design and implement a smart contract to facilitate an automatic, autonomous, and auditable rational reverse auction mechanism among edge nodes. Moreover, we leverage the payment channel technique to enable credible, fast, low-cost, and high-frequency payment transactions between requesters and edge nodes. Simulation results show that the proposed reverse auction mechanism can achieve the properties, including budget feasibility, truthfulness, and computational efficiency.

【Keywords】Blockchain; Internet of Things; Computational modeling; Edge computing; Peer-to-peer computing; Training; Smart contracts; Auction; blockchain; edge computing; Internet of Things (IoT); trade market

【发表时间】2021 44607

【收录时间】2022-01-02

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