Federated Distributed Deep Reinforcement Learning for Recommendation-enabled Edge Caching

This paper investigates a content recommendation-based edge caching method in multi-tier edge-cloud networks while considering content delivery and cache replacement decisions as well as bandwidth allocation strategies. First, we formulate the optimization problem with the goal of minimizing long-term content delivery delay. Second, we model the optimization problem as a Partially Observable Markov Decision Process, and propose a Federated Distributed Deep Deterministic Policy Gradient-based method (FD3PG) to solve the corresponding problem. In conclusion, simulation results demonstrate that the proposed FD3PG achieves not only a much lower content delivery delay but also a higher cache hit rate compared with other baselines in various scenarios.