Privacy and Security Challenges in Federated Learning for UAV Systems: A Systematic Review
Abstract
Unmanned Aerial Vehicles have become indispensable assets across various sectors, leveraging their mobility and data collection capabilities. However, privacy and security concerns have fueled interest in Federated Learning as a potential solution. Federated learning (FL), being decentralized and collaborative, holds promise in addressing the privacy risks inherent in centralized data processing while enhancing model performance. In this review, we explore the privacy and security implications of FL in UAV ecosystems. We highlight FL’s potential to mitigate privacy risks by aggregating model updates locally, minimizing data transmission needs. In addition, we examine security challenges and evaluate protective mechanisms. Through a systematic review of the literature, we identified gaps and proposed future research directions, with the aim of improving the security and privacy of FL in UAV applications. Our findings highlight advanced strategies, such as secure aggregation, adversarial defenses, and lightweight cryptographic techniques, to mitigate privacy and security threats in Federated Learning-based UAV. They also underscore the need for real-world validations and regulatory frameworks to ensure resilient and ethically governed deployments in UAV networks.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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