Blockchain-Empowered Federated Learning Approach for an Intelligent and Reliable D2D Caching Scheme

【Author】 Cheng, Runze; Sun, Yao; Liu, Yijing; Xia, Le; Feng, Daquan; Imran, Muhammad Ali

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Cache-enabled device-to-device (D2D) communication is a potential approach to tackle the resource shortage problem. However, public concerns of data privacy and system security still remain, which thus arises an urgent need for a reliable caching scheme. Fortunately, federated learning (FL) with a distributed paradigm provides an effective way to privacy issue by training a high-quality global model without any raw data exchanges. Besides the privacy issue, blockchain can be further introduced into the FL framework to resist the malicious attacks occurred in D2D caching networks. In this study, we propose a double-layer blockchain-based deep reinforcement FL (BDRFL) scheme to ensure privacy-preserved and caching-efficient D2D networks. In BDRFL, a double-layer blockchain is utilized to further enhance data security. Simulation results first verify the convergence of the BDRFL-based algorithm, and then demonstrate that the download latency of the BDRFL-based caching scheme can be significantly reduced under different types of attacks when compared to some existing caching policies.

【Keywords】Device-to-device communication; Blockchains; Reliability; Training; Data models; Data privacy; Privacy; Blockchain; device-to-device (D2D) caching; federated learning (FL)

【发表时间】2022 JUN 1

【收录时间】2022-06-15

【文献类型】理论性文章

【主题类别】

区块链技术-协同技术-联邦学习