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

Yunfeng Zhao; Yixin Liu; Qingfeng Chen; Shiyuan Li; Yue Tan; Shirui Pan

IJCAI 20260 citations

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

Yunfeng Zhao, Yixin Liu, Qingfeng Chen, Shiyuan Li, Yue Tan, Shirui Pan

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 compared to state-of-the-art methods.

Data Mining: Anomaly/outlier detectionData Mining: Mining graphsMachine Learning: Federated learningMachine Learning: Unsupervised learning

BibTeX

@inproceedings{ijcai2026_fedcigarapersona,
  title = {FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-Level Anomaly Detection},
  author = {Yunfeng Zhao and Yixin Liu and Qingfeng Chen and Shiyuan Li and Yue Tan and Shirui Pan},
  booktitle = {IJCAI 2026},
  year = {2026}
}