【Author】
Cheng, Runze; Sun, Yao; Liu, Yijing; Xia, Le; Feng, Daquan; Imran, Muhammad Ali
【Source】IEEE INTERNET OF THINGS JOURNAL
【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)
【标题】区块链支持的联邦学习方法，用于智能和可靠的D2D缓存方案
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