Federated Online Learning Aided Multi-Objective Proactive Caching in Heterogeneous Edge Networks

To address the drastic increase in multimedia traffic volume, mobile edge caching (MEC) has been exploited to reduce redundant data transmissions by equipping computation and storage capacity at the edge network. Previous works on learning-based caching problems often only concern pre-storing popular contents to satisfy users’ demands. In this work, we investigate the cache strategy design problem with two possibly conflicting objectives, namely, cache hit and cache profit, in heterogeneous multi-MEC server networks when content profiles are unknown. We then formulate this multi-objective caching problem as a Multi-agent Multi-objective Combinatorial Multi-Armed bandit (MMC-MAB) problem and propose a two-step caching framework that estimates content properties first and then optimizes cache placement. Specifically, to accommodate the system heterogeneity in estimation, we utilize an adaptive federated learning-based estimation to approach the unknown content popularity and profit profiles, which wisely use both local and external observations by adjusting the mixing factors. To address the multiple objective optimizations, we propose two effective methods, based on individual dominance and combinatorial dominance, to achieve adequate Pareto-optimal cache placement. Both theoretical results and comprehensive experiments clearly validate the effectiveness and efficiency of our proposed approaches.