Federated Multi-Armed Bandit Learning for Caching in UAV-aided Content Dissemination

Abstract

This paper proposes an Unmanned aerial vehicle (UAV) aided content management system in communication-challenged disaster scenarios. Without cellular infrastructure in such scenarios, communities of stranded users can be provided access to situation-critical content using a hybrid network of static and traveling UAVs. A set of relatively static anchor UAVs with vertical as well as lateral links can provide content access to its local users. A set of ferrying UAVs with only lateral links, but with wider mobility, can provision content to users while visiting their communities. The objective is to design a content dissemination system that learns caching policies on-the-fly for maximizing content availability to the users. This paper proposes a distributed Federated Multi-Armed Bandit (MAB) Learning technique for UAV-caching decisions in the presence of geo-temporal differences in content popularity and heterogeneity in content demands. The proposed mechanism is able to combine the expected reward maximization attribute of Multi-Armed Bandit, and distributed intelligence sharing nature of Federated Learning for caching decision at the UAVs. It is demonstrated that Federated aggregation of individual MAB models can improve system performance while making the learning fast and adaptive. This analysis is done for different user-specified tolerable access delay, heterogeneous popularity distributions, and inter-community geographical characteristics. The paper does functional verification and performance evaluation of the proposed caching framework under a wide range of network size, UAV distribution, content popularity, and ferrying UAV trajectories.