A Wolf in Sheep’s Clothing: Unveiling a Stealthy Backdoor Attack in Subgraph Federated Learning

Summary

Researchers propose BEEF, an end-to-end backdoor attack against subgraph federated learning for node classification, where subgraphs are distributed across devices. BEEF uses a trigger generator trained jointly with the backdoored model, crafting adversarial perturbations as triggers that cause misclassification without changing model parameters. Evaluations across eight datasets, four models, five attacks and six aggregation methods report effectiveness against GNNs with minimal impact on normal data performance.

Topics

• Adversarial machine learning

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