Yige Liu

Label inference attacks in vertical federated learning don't work because bottom models are good at representing labels, but because of feature-label distribution alignment, opening the door to simple, effective defenses.

Trigger-based defenses offer a false sense of security in federated learning, as this new attack shows backdoors can be implanted without any explicit triggers, achieving 2-50x better performance than trigger-based attacks.