Towards Efficient Edge Caching: A Federated Reinforcement Learning Approach in Cloud-Edge-End Networks

In this paper, we address the problem of recommendation-enabled caching for cloud-edge-end collaborative networks. We leverage collaborative filtering techniques to predict user content preferences and construct a content recommendation matrix. To minimize content delivery delay, we model the cache replacement process as a MDP and propose an enhanced FDDPG algorithm for optimization. Experimental results demonstrate that our approach significantly reduces average delivery latency and improves the cache hit rate compared to existing methods.