Faizan Qamar

Edge- Federated Graph Anomaly Detection With Self-Supervised Representation Learning for IoT Networks

This paper presents an Edge Federated Graph Anomaly Detection (E-FGAD) framework for IoT environments that combines centralized self-supervised pre-training with distributed supervised learning over edge embeddings that outperforms centralized and federated baselines in detecting attacks while preserving privacy.