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

2025-01-15

AI Summary

To address information isolation caused by post-disaster communication outages, this paper proposes a collaborative content dissemination architecture for UAV swarms, integrating static anchor UAVs with mobile micro-UAVs to enable both vertical and horizontal air-ground coordination as well as inter-UAV information sharing. We innovatively design a distributed federated multi-armed bandit (F-MAB) learning framework that jointly models spatiotemporal content popularity and employs a selective caching algorithm. This framework enables cross-UAV knowledge aggregation while suppressing cache redundancy—all while preserving user preference diversity. Experimental results demonstrate significant improvements in critical content reachability and network content availability. The approach exhibits strong robustness and adaptability across varying swarm scales and dynamic content popularity scenarios.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems

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.

Problem

• Disaster Communication
• Information Management
• Time-location Dependent Needs

Innovation

• Drone Teams
• Adaptive Content Management
• Information Dissemination