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

• URL: http://arxiv.org/abs/2501.09146v1
• Date: Wed, 15 Jan 2025 20:55:13 GMT
• Title: Towards Federated Multi-Armed Bandit Learning for Content Dissemination using Swarm of UAVs
• Authors: Amit Kumar Bhuyan, Hrishikesh Dutta, Subir Biswas,
• Abstract summary: The proposed architecture leverages a hybrid network of stationary anchor UAVs and mobile Micro-UAVs for ubiquitous content dissemination. The focus is on developing a content dissemination system that dynamically learns optimal caching policies to maximize content availability. A Selective Caching Algorithm is also introduced to reduce redundant content replication by incorporating inter-UAV information sharing.
• Score: 2.3076690318595676
• License:
• 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.

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