Model Hijacking Attack in Federated Learning

Summary

Researchers present HijackFL, the first model hijacking attack against the global model in federated learning, where multiple clients train a shared model without sharing data. The adversary computes pixel-level perturbations on their unmodified local model so that hijacking samples align with original samples in feature space, causing the global model to perform a different task without the server or benign clients noticing. Experiments report 92.75% attack success versus 10% for baselines.

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