FLAS: A Federated Learning Framework for Adaptive Security in Edge-Driven UAV Networks

Unmanned Aerial Vehicle (UAV) networks have emerged as a transformative technology with applications ranging from surveillance to disaster response. These networks rely on a decentralized architecture where UAVs communicate with each other and with ground stations. But because UAV networks are naturally weak, especially at the edges, strong security measures are needed to keep private data safe and make sure operations run smoothly. The dynamic and distributed nature of UAV networks poses unique security concerns, including data breaches, unauthorized access, and tampering. To address the security challenges prevalent in UAV networks, we propose a Federated Learning-based Adaptive Security (FLAS) Framework using fully homomorphic encryption (FHE). In the FLAS framework, federated learning (FL) allows UAVs to share knowledge while preserving data privacy to defend against security threats. Its integration ensures that collaborative learning occurs in edge-driven UAV environments without exposing raw data. FHE enables secure computations on encrypted data. The proposed FLAS approach ensures that security measures evolve dynamically to counter emerging threats. Our proposed method not only enhances the security posture of UAV networks but also optimizes resource utilization by distributing the learning process across the network's edge.