Mitigating Sparsity and Heterogeneity in Unsupervised Federated Graph Anomaly Detection

Federated graph anomaly detection shows promising potential in academia and industry since it solves the limitations of traditional centralized GAD. However, existing methods suffer from local sparsity, intrinsic distribution shifts, and label reliance issues, resulting in limited semantic information in local clients, strong heterogeneity across clients, and vulnerability to label quality. To address these issues, we propose an unsupervised federated graph anomaly detection (UnFedGAD) framework, which enhances local semantic information through cross-client topology suture and addresses heterogeneity via personalized bidirectional parameter updating. Specifically, UnFedGAD discovers adequate semantics via cross-client communication with privacy protection, identifying the nearest neighboring client based on prototype similarity for each client. In addition, UnFedGAD employs prototype similarities to compute adaptive weights for global consensus generation and introduces a novel client updating mechanism to strike a dynamic balance between local and global models, effectively conquering client heterogeneity. To eliminate the dependence on labels, UnFedGAD generates pseudo-labels based on local-global consistency estimation for unsupervised training. Extensive experiments on eight public datasets demonstrate the effectiveness and advancement of UnFedGAD compared with nine baselines.