FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
For federated learning applications requiring graph-level anomaly detection, this work addresses privacy and data heterogeneity issues with a personalized reconstruction approach.
FedCIGAR tackles federated graph-level anomaly detection without synthetic anomalies, using a reconstruction-based paradigm on normal graphs and adaptive clustering to handle data heterogeneity, achieving superior performance and robustness over state-of-the-art methods.
Graph-level anomaly detection (GLAD) is crucial for ensuring the reliability of graph-driven applications by identifying abnormal graphs that deviate from the majority. Considering the privacy concerns in distributed scenarios, federated graph-level anomaly detection (FedGLAD) has emerged as a promising solution to enable collaborative detection without sharing raw data. However, existing methods suffer from poor generalization due to the reliance on unrealistic synthetic anomalies and insufficient personalization capabilities under data heterogeneity. To address these challenges, we propose a novel Federated graph-level anomaly detection approach with Cluster-adaptIve GAted Reconstruction (FedCIGAR). Specifically, we design a reconstruction-based paradigm trained on normal graphs to avoid synthetic data. Furthermore, we introduce a client-side node contribution gating mechanism and a server-side sliding window-based clustering strategy to tackle data heterogeneity. Extensive experiments demonstrate that FedCIGAR achieves superior performance and robustness in contrast to state-of-the-art methods.