A Privacy-preserved D2D Caching Scheme Underpinned by Blockchain-enabled Federated Learning

Cheng, R. , Sun, Y. , Liu, Y., Xia, L. , Sun, S. and Imran, M. A. (2021) A Privacy-preserved D2D Caching Scheme Underpinned by Blockchain-enabled Federated Learning. In: 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 07-11 Dec 2021, ISBN 9781728181042 (doi: 10.1109/GLOBECOM46510.2021.9685849)

Abstract

Cache-enabled device-to-device (D2D) communication has been widely deemed as a promising approach to tackle the unprecedented growth of wireless traffic demands. Recently, tremendous efforts have been put into designing an efficient caching policy to provide users better quality of service. However, public concerns of data privacy still remain in D2D cache sharing networks, which thus arises an urgent need for a privacy-preserved caching scheme. In this study, we propose a double-layer blockchain-based federated learning (DBFL) scheme with the aim of minimizing the download latency for all users in a privacy-preserving manner. Specifically, in the sublayer, the devices within the same coverage area run a federated learning (FL) to train the caching scheme model for each area separately without exchange of local data. The model parameters for each area are recorded in sublayer chains with Raft consensus mechanism. Meanwhile, in the main layer, a mainchain based on practical Byzantine fault tolerance (PBFT) mechanism is used to resist faults and attacks, thus securing the reliability of FL updates. Only the reliable area models authorized by the mainchain are utilized to update the global model in the main layer. Numerical results show the convergence, as well as the gain of download latency of the proposed DBFL caching scheme when compared with several traditional schemes.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Xia, Le and Imran, Professor Muhammad and Sun, Dr Yao and CHENG, RUNZE
• Authors: Cheng, R., Sun, Y., Liu, Y., Xia, L., Sun, S., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• ISBN: 9781728181042
• Published Online: 02 February 2022
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in 2021 IEEE Global Communications Conference (GLOBECOM)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy