ResFL-UAV++ for robust and resource-constrained secure federated learning with adversarial defense in UAV systems
TL;DR
The proposed ResFL-UAV++ framework achieves over 98% accuracy in adversarial UAV detection while introducing less than 4% system overhead, and adversarial training using the Fast Gradient Sign Method (FGSM) enhances reliability and data confidentiality while incurring minimal computational overhead.
Abstract
Federated Learning (FL) enables privacy-preserving collaborative model training for Unmanned Aerial Vehicles (UAVs). However, its decentralized nature makes it vulnerable to adversarial attacks such as model poisoning, label flipping, and backdoor attacks. To address these challenges in resource-constrained UAV environments, this study proposes ResFL-UAV++, a lightweight and secure FL framework incorporating a multi-layer defense mechanism. The framework integrates a multi-metric anomaly detection module based on cosine similarity, L2-Norm Filtering, and Temporal Update Consistency (TUC) to identify malicious UAV updates. A hybrid robust aggregation strategy combining Trimmed Mean and Krum mitigates adversarial effects while preserving model convergence. Additionally, adversarial training using the Fast Gradient Sign Method (FGSM), together with Differential Privacy (DP), enhances reliability and data confidentiality while incurring minimal computational overhead. Experimental evaluation on the HIT-UAV Infrared Thermal dataset and the WebUAV-3M demonstrates that ResFL-UAV++ achieves 98% accuracy under adversarial conditions. The framework reduces the Backdoor Attack Success Rate (ASR) to below 20%. Furthermore, it achieves over 98% accuracy in adversarial UAV detection while introducing less than 4% system overhead. These results demonstrate the effectiveness and practicality of ResFL-UAV++ for secure FL in UAV environments.