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.

In communication-deprived disaster scenarios, this paper introduces a Micro-Unmanned Aerial Vehicle (UAV)- enhanced content management system. In the absence of cellular infrastructure, this system deploys a hybrid network of stationary and mobile UAVs to offer vital content access to isolated communities. Static anchor UAVs equipped with both vertical and lateral links cater to local users, while agile micro-ferrying UAVs, equipped with lateral links and greater mobility, reach users in various communities. The primary goal is to devise an adaptive content dissemination system that dynamically learns caching policies to maximize content accessibility. The paper proposes a decentralized Top-k Multi-Armed Bandit (Top-k MAB) learning approach for UAV caching decisions, accommodating geotemporal disparities in content popularity and diverse content demands. The proposed mechanism involves a Selective Caching Algorithm that algorithmically reduces redundant copies of the contents by leveraging the shared information between the UAVs. It is demonstrated that Top-k MAB learning, along with selective caching algorithm, can improve system performance while making the learning process adaptive. The paper does functional verification and performance evaluation of the proposed caching framework under a wide range of network size, swarm of micro-ferrying UAVs, and heterogeneous popularity distributions.