In disaster scenarios with compromised communication infrastructure, Unmanned Aerial Vehicles (UAVs) can provide ad hoc connectivity for resilient information dissemination. This thesis develops a hierarchical UAV-assisted framework of federated multi-armed bandit learning for post-disaster content dissemination. The developed framework incorporates a two-tier UAV hierarchy consisting of Anchor UAVs (A-UAVs) with high-cost backhaul connectivity, and more mobile Micro-Ferrying UAVs (MF-UAVs) without backhaul links. Such a hierarchy allows for strategic offloading of storage-intensive tasks to A-UAVs, while leveraging the mobility of MF-UAVs to dynamically ferry content across disconnected user clusters. By integrating trajectory-aware selective caching strategies into UAV operations, the framework aligns aerial mobility patterns with evolving spatio-temporal content demands. Algorithmic innovation of the framework stems from a federated bandit and stateless reinforcement learning paradigm, which enables UAVs to collaboratively learn content popularity profiles, and adapt caching policies based on localized user request patterns. Unlike centralized methods, the federated approach preserves data locality and minimizes inter-UAV communication overhead, which is critical in bandwidth- and energy-constrained post-disaster environments. The multi-armed bandit learning mechanism utilizes a multi-dimensional reward feedback architecture that captures content relevance, inter-UAV delivery latency, and caching diversity across disjointed and isolated user communities. The thesis also explores the interplay between UAV energy budgets, caching capacities, and mission-critical delivery constraints such as quality-of-service expectations in terms of tolerable access delay. To summarize, the research in this thesis bridges multi-agent learning with mission-oriented aerial networking towards developing solutions for smart content dissemination in networks with sparse connectivity.
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