Federated Learning

Defending Batch-Level Label Inference and Replacement Attacks in Vertical Federated Learning

Explores vulnerabilities in VFL models to label inference and backdoor attacks and proposes effective defenses like CAE and DCAE.

Label Inference Attacks Against Vertical Federated Learning

Evaluates privacy risks of vertical federated learning (VFL) and proposes label inference attacks with outstanding performance, highlighting vulnerabilities and defense limitations.