Unmanned Aerial Vehicles (UAVs) are becoming increasingly vital for applications such as disaster response, environmental monitoring, infrastructure inspection, and cybersecurity. These airborne platforms can collect diverse types of data in real time, offering valuable input for training high-performance machine learning models. However, conventional machine learning techniques often rely on centralized training paradigms that require transmitting all raw data from UAVs to centralized servers: an approach that is often impractical due to privacy concerns, limited bandwidth, and latency constraints. To address these challenges, this project introduces a novel distributed learning framework based on federated learning, which enables UAVs to collaboratively train models while keeping their raw data local. In particular, the project’s novelties are centered on enabling multi-modal federated learning across UAV networks, where each UAV may observe a distinct combination of data modalities/types (e.g., imagery, environmental readings, or network traffic). This data modality imbalance introduces new challenges in learning coordination, model convergence, and system-level optimization. To this end, the proposed research develops a unified approach that addresses modality imbalance, adversarial threats, and system-level heterogeneity in computation, communication, and storage. The project's broader significance and importance will thus lie in advancing the resilience and reliability