FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection

2026-05-10

AI Summary

This work addresses the limitations of existing federated graph-level anomaly detection methods, which suffer from weak generalization and insufficient personalization due to reliance on synthetic anomalies and data heterogeneity. To overcome these issues, the authors propose an unsupervised federated approach based on normal graph reconstruction that eliminates the need for unrealistic synthetic anomaly training data. The method incorporates a client-side node contribution gating mechanism and a server-side sliding-window clustering strategy to effectively handle data heterogeneity while preserving privacy. Through cluster-adaptive gated reconstruction, the model enables efficient collaboration and personalized anomaly detection without sharing raw data. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art approaches across multiple benchmark datasets, achieving superior detection performance and robustness.

Abstract

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.

Problem

• federated graph-level anomaly detection
• data heterogeneity
• personalization
• synthetic anomalies
• graph-level anomaly detection

Innovation

• federated learning
• graph-level anomaly detection
• personalized reconstruction
• data heterogeneity
• gated mechanism