From Flight to Intelligence: UAV Applications and Federated Learning Approaches

ABSTRACT

Unmanned aerial vehicles (UAVs) have quickly integrated themselves as fundamental technologies in nearly every field, including surveillance, delivery, precision agriculture and even disaster management. This survey looks at the application of UAVs in communication systems, control systems and the 3D reconstruction of objects and utilises image data techniques. UAVs operate in real time on command, requiring strong and dependable communication for multiple UAVs to operate in swarming and beyond‐visual‐line‐of‐sight missions. Control systems incorporating AI‐based autonomy need robust path planning in dynamically and uncertainly hostile environments. Furthermore, precision 3D reconstruction allows accurate environmental modelling to inspect urban planning infrastructure. Despite technological advancements, UAV systems face several challenges, including bandwidth limitations, energy constraints, latency and data privacy concerns. The survey analyses the growing importance of federated learning (FL) in UAV networks as an approach to model training that ensures data privacy and security. This paper represents different applications and challenges in UAVs. This paper also describes different FL‐based methods and techniques to overcome various challenges in UAVs. This research analyses emerging trends and addresses unfulfilled needs to provide an actionable guide to build upon for further advancement in UAV networks, urging for new enhanced adaptability, flexible scalability and intelligent multifunctional solutions for wide‐ranging future uses.