FedGAD: Real-Time Anomaly Detection and Adaptive Defense With Federated GANs in 6G

The advent of 6G networks brings forth unprecedented security challenges due to massive device interconnectivity and rapidly evolving cyber threats. Existing centralized anomaly detection and defense systems fall short in scalability, privacy protection, and responsiveness, rendering them ineffective for next-generation network environments. In this work, we propose FedGAD (Federated Generative Anomaly Detection), a technically novel framework that tightly integrates federated learning with generative adversarial training to enable distributed, privacy-preserving, and adaptive anomaly detection. Unlike prior approaches, FedGAD introduces a dual-model architecture where a federated generator synthesizes hard-to-detect adversarial anomalies, while local discriminators are collaboratively trained across edge nodes to improve detection sensitivity without sharing raw data. This adversarial co-training process enhances model robustness against zero-day attacks and adapts in real-time to changing threat landscapes. Extensive experiments show that FedGAD outperforms state-of-the-art techniques in terms of detection accuracy, adaptability, and computational efficiency, offering a scalable and resilient defense paradigm tailored for the dynamic security demands of 6G networks.