(ICWS 2025) A Federated Deep Reinforcement-Learning-based Method for User-interest-informed Dynamic Caching in Mobile Edge Computing

To meet the rapidly growing demands for services and applications in Mobile Edge Computing (MEC) environments, there is an increasing need to alleviate backhaul network pressure and enhance user experience. However, the dynamic nature of user mobility, fluctuating content popularity, and varying interest similarities within and across user communities pose significant challenges in designing efficient caching strategies. To address these challenges, this paper proposes a user interest-informed edge caching method by using a dynamic User-interest-based Clustring Caching (UCC) model. The proposed framework includes an improved density-based spatial clustering algorithm which employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a caching decision algorithm which adopts a federated deep reinforcement learning model for yielding high-quality and dynamic caching schedules. Experiments based on real-world urban taxi datasets and user interest datasets clearly demonstrates that the proposed method outperforms several existing algorithms across multiple performance metrics.