Good Gradients Poison Your Model: Evading Defenses in Federated Learning via Boundary-adaptive Perturbation
Federated learning (FL) allows for collaborative model training while preserving data privacy, but its distributed nature makes it vulnerable to poisoning attacks. Existing defense methods typically rely on using gradients from multiple clients to define a trusted region, selecting only the trustwor