BESIFL: Blockchain-Empowered Secure and Incentive Federated Learning Paradigm in IoT

【Author】 Xu, Yajing; Lu, Zhihui; Gai, Keke; Duan, Qiang; Lin, Junxiong; Wu, Jie; Choo, Kim-Kwang Raymond

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Federated learning (FL) offers a promising approach to efficient machine learning with privacy protection in distributed environments, such as Internet of Things (IoT) and mobile-edge computing (MEC). The effectiveness of FL relies on a group of participant nodes that contribute their data and computing capacities to the collaborative training of a global model. Therefore, preventing malicious nodes from adversely affecting the model training while incentivizing credible nodes to contribute to the learning process plays a crucial role in enhancing FL security and performance. Seeking to contribute to the literature, we propose a blockchain-empowered secure and incentive FL (BESIFL) paradigm in this article. Specifically, BESIFL leverages blockchain to achieve a fully decentralized FL system, where effective mechanisms for malicious node detections and incentive management are fully integrated in a unified framework. The experimental results show that the proposed BESIFL is effective in improving FL performance through its protection against malicious nodes, incentive management, and selection of credible nodes.

【Keywords】Blockchains; Training; Servers; Peer-to-peer computing; Collaborative work; Data models; Computational modeling; Blockchain; consensus algorithm; federated learning (FL); incentive mechanism; Internet of Things (IoT)

【发表时间】2023 15-Apr

【收录时间】2023-05-25

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-物联网