Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching

• Published in: IEEE journal on selected areas in communications Vol. 39; no. 1; pp. 154 - 169
• Main Authors: Wang, Xiaofei, Li, Ruibin, Wang, Chenyang, Li, Xiuhua, Taleb, Tarik, Leung, Victor C. M.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; attention-weighted federated learning; Caching; Cloud computing; Collaboration; Computational modeling; Cooperation; Data models; Deep learning; deep reinforcement learning; Delays; device to device; Device-to-device communication; Edge caching; Integer programming; Linear programming; Machine learning; Markov processes; Mixed integer; Multimedia; Optimization; Servers; Storage capacity; Training; Wireless networks
• DOI: 10.1109/JSAC.2020.3036946
• ISSN: 0733-8716

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic.