GenFed-IDS: A Lightweight Federated Generative AI Framework for UAV Anomaly Detection in Rescue Operations

Unmanned Aerial Vehicles are increasingly deployed in consumer applications such as logistics, disaster recovery, and surveillance, yet their wireless communication links remain highly vulnerable to cyber-attacks. Traditional intrusion detection systems (IDS) often struggle in UAV environments due to resource constraints, dynamic network conditions, and scarcity of labeled datasets. To address these challenges, we propose a Generative AI-enabled lightweight IDS framework tailored for UAV communication networks. The framework integrates hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN-GRU) autoencoders with generative augmentation and knowledge distillation, achieving high accuracy while maintaining computational efficiency. Explainability is incorporated via SHapley Additive exPlanations (SHAP) analysis to ensure trustworthy and interpretable decision-making. Experimental evaluations on a multimodal UAV dataset demonstrate state-of-the-art performance with 99.49% accuracy, 99.48% recall, and AUROC of 0.9999, alongside a student model that reduces model size, inference latency, and memory footprint by nearly 50%. Comparative results confirm that the proposed framework outperforms recent IDS baselines in both detection capability and lightweight deployment, offering a practical and scalable solution for next-generation UAV communication networks.