Federated Adaptive Bandits Aided Caching for Heterogeneous Edge Servers with Uncertainty

Caching popular content at edge servers is a promising way to achieve high quality of experience for the wireless edge network. However, the design of an effective cache placement scheme still faces two key challenges: 1) content popularity profiles may be unknown in advance. Some online learning techniques can be incorporated to tackle this uncertainty. 2) content popularity profiles may be heterogeneous among different edge servers. Naively utilizing feedback collected by others may substantially hurt the local estimation if there are large disparities between the popularities. Therefore, an adptive information aggregation protocol is needed. In this paper, we first formulate the caching problem as a multi-agent multi-play bandits problem with heterogeneous reward distributions. We then propose a federated adaptive online learning algorithm. Specifically, each MES employs a model mixture technique to aggregate local user feedback and the knowledge captured by the central server. Our theoretical results show that the upper bound on the cache hit loss (e.g., regret) depends on the heterogeneity and information sharing across MESs. The simulation results demonstrate the effectiveness of our method against baseline schemes on both regrets and cache hit rate.