AI RESEARCH
FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
arXiv CS.LG
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ArXi:2605.09428v1 Announce Type: new 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.