Towards Federated Multi-Armed Bandit Learning for Content Dissemination using Swarm of UAVs

TL;DR

This paper presents a UAV-enabled content dissemination system that uses federated multi-armed bandit learning to optimize caching policies for disaster scenarios, enhancing content availability and adaptability.

Contribution

It introduces a novel adaptive content dissemination framework based on distributed federated multi-armed bandit learning for UAV networks in disaster scenarios.

Findings

Effective content caching decisions based on geo-temporal popularity.

Improved content availability across UAV swarms.

Reduced redundant content replication through inter-UAV sharing.

Abstract

This paper introduces an Unmanned Aerial Vehicle - enabled content management architecture that is suitable for critical content access in communities of users that are communication-isolated during diverse types of disaster scenarios. The proposed architecture leverages a hybrid network of stationary anchor UAVs and mobile Micro-UAVs for ubiquitous content dissemination. The anchor UAVs are equipped with both vertical and lateral communication links, and they serve local users, while the mobile micro-ferrying UAVs extend coverage across communities with increased mobility. The focus is on developing a content dissemination system that dynamically learns optimal caching policies to maximize content availability. The core innovation is an adaptive content dissemination framework based on distributed Federated Multi-Armed Bandit learning. The goal is to optimize UAV content caching decisions based on geo-temporal content popularity and user demand variations. A Selective Caching Algorithm is also introduced to reduce redundant content replication by incorporating inter-UAV information sharing. This method strategically preserves the uniqueness in user preferences while amalgamating the intelligence across a distributed learning system. This approach improves the learning algorithm's ability to adapt to diverse user preferences. Functional verification and performance evaluation confirm the proposed architecture's utility across different network sizes, UAV swarms, and content popularity patterns.