Edge- Federated Graph Anomaly Detection With Self-Supervised Representation Learning for IoT Networks
The Internet of Things (IoT) plays a vital role in the digital age by interconnecting numerous heterogeneous devices. This complexity and ubiquity expose IoT networks to diverse and sophisticated cyber threats. IoT environments require rigorous collaborative intrusion detection system (IDS) that operates in distributed environments and heterogeneous data traffic. While federated learning offers a promising, privacy-conscious training model, most traditional approaches to IDS fail to detect the structural relationships between connected entities. On the other hand, Graph Neural Networks (GNNs) have attracted significant attention in Network Intrusion Detection Systems (NIDS) for their effectiveness in modeling complex network traffic flows in real-world environments. However, existing GAD methods are generally designed for centralized training, thereby posing privacy leakage risks. Despite progress, current mainstream Federated Graph anomaly detection (FGAD) methods still face challenges. A key limitation is that most existing approaches focus on node-level analysis while disregarding inter-node relationships making them ineffective against sophisticated attacks. To tackle this problem, 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. During the supervised phase, parameters are optimized in a federated manner using FedAvg with FedProx and server momentum. Our framework operates on graphs, where nodes represent IP endpoints and edges represent NetFlow records with traffic statistics as features, aiming to capture the flow of interactions between entities. We evaluate experiments on two real-world datasets, NF-BoT-IoT-v3 and NF-ToN-IoT-v3, in both binary and multi-class settings. E-FGAD achieves a maximum detection accuracy of 99.32%, a Macro-F1 of 89.73%, and a Weighted-F1 of 99.2%. Our framework outperforms centralized and federated baselines, demonstrating its effectiveness in detecting attacks while preserving privacy.