FedCIGAR improves federated graph anomaly detection via normal-graph reconstruction, client node gating, and server sliding-window clustering, claiming better performance than prior methods under data heterogeneity.
An efficient framework for clustered federated learning.Advances in neural infor- mation processing systems, 33:19586–19597
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FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
FedCIGAR improves federated graph anomaly detection via normal-graph reconstruction, client node gating, and server sliding-window clustering, claiming better performance than prior methods under data heterogeneity.