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

TL;DR

FedCIGAR is a federated graph anomaly detection method that improves personalization and robustness by using reconstruction and clustering strategies without synthetic anomalies.

Contribution

It introduces FedCIGAR, a novel federated approach combining reconstruction and clustering to enhance anomaly detection under data heterogeneity.

Findings

FedCIGAR outperforms existing methods in accuracy and robustness.

The approach effectively handles data heterogeneity in federated settings.

Reconstruction-based training avoids the need for synthetic anomalies.

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.