Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning
Federated learning allows hospitals to train AI models collaboratively while keeping patient data private. Model Inversion Attacks try to steal data from shared updates. Aegis defends against these attacks by adding a masking gradient from synthetic, task-relevant data to the real update. This pushes the effective batch size beyond the attack's limit, neutralizing major threats without harming model accuracy.