Model Hijacking Attack in Federated Learning

Researchers discovered a new attack called HijackFL that can hijack machine learning models in federated learning systems (where multiple computers train a shared model without sharing raw data). The attack works by adding tiny pixel-level changes to input samples so the model misclassifies them as something else, while appearing normal to the server and other participants, achieving much higher success rates than previous methods.