Federated Deep Reinforcement Learning for Internet of Things with Decentralized Cooperative Edge Caching

Xiaofei Wang, Chenyang Wang, Xiuhua Li*, Victor C.M. Leung, Tarik Taleb

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

63 Citations (Scopus)
601 Downloads (Pure)


Edge caching is an emerging technology for addressing massive content access in mobile networks to support rapidly growing Internet-of-Things (IoT) services and applications. However, most current optimization-based methods lack a self-adaptive ability in dynamic environments. To tackle these challenges, current learning-based approaches are generally proposed in a centralized way. However, network resources may be overconsumed during the training and data transmission process. To address the complex and dynamic control issues, we propose a federated deep-reinforcement-learning-based cooperative edge caching (FADE) framework. FADE enables base stations (BSs) to cooperatively learn a shared predictive model by considering the first-round training parameters of the BSs as the initial input of the local training, and then uploads near-optimal local parameters to the BSs to participate in the next round of global training. Furthermore, we prove the expectation convergence of FADE. Trace-driven simulation results demonstrate the effectiveness of the proposed FADE framework on reducing the performance loss and average delay, offloading backhaul traffic, and improving the hit rate.

Original languageEnglish
Article number9062302
Pages (from-to)9441-9455
Number of pages15
JournalIEEE Internet of Things Journal
Issue number10
Publication statusPublished - Oct 2020
MoE publication typeA1 Journal article-refereed


  • Cooperative caching
  • deep reinforcement learning (DRL)
  • edge caching
  • federated learning
  • hit rate
  • Internet of Things (IoT)


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