Privacy and Security Challenges in Federated Learning for UAV Systems: A
Comprehensive Review
Abstract
Unmanned Aerial Vehicles (UAVs) have become indispensable assets
in various sectors, leveraging their mobility and data collection
capabilities. However, privacy and security concerns have fueled
interest in Federated Learning (FL) as a solution. FL, decentralized and
collaborative, offers promise in addressing privacy risks inherent in
centralized data processing while enhancing model performance. In this
review, we explore FL’s privacy and security implications in UAV
ecosystems. We highlight FL’s potential to mitigate privacy risks by
aggregating model updates locally, minimizing data transmission needs.
Additionally, we examine security challenges and evaluate protective
mechanisms. Through a systematic literature review, we identify gaps and
propose future research directions, aiming to enhance the security and
privacy of FL in UAV applications.
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