Adaptive Federated Deep Reinforcement Learning for Proactive Content Caching in Edge Computing

• Published in: IEEE transactions on parallel and distributed systems Vol. 33; no. 12; pp. 4767 - 4782
• Main Authors: Qiao, Dewen, Guo, Songtao, Liu, Defang, Long, Saiqin, Zhou, Pengzhan, Li, Zhetao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive algorithms; Caching; Cloud computing; Content caching; Data models; Deep learning; deep reinforcement learning; Delays; Edge computing; Efficiency; Feature extraction; federated learning; Internet of Things; Iterative methods; Machine learning; Optimization; Reinforcement learning; resource constraint; Servers; Training

With the aggravation of data explosion and backhaul loads on 5 G edge network, it is difficult for traditional centralized cloud to meet the low latency requirements for content access. The federated learning ( F L)-based p roactive content c aching (FPC) can alleviate the matter by placing content...