Toward More Practical Label Inference Attacks Against Graph-Based Vertical Federated Learning

Summary

Researchers propose Knowledge Transfer Attack (KTA), a label inference attack against graph-based vertical federated learning (GVFL). KTA infers an active party's private labels using only auxiliary graphs from non-training domains, without the exact class space, the top model, or labeled auxiliary data that prior attacks assumed. The authors report that experiments confirm KTA's effectiveness and its superiority over state-of-the-art attacks.

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