FILTER: A Framework for Defending Against Backdoor Attacks in Vertical Federated Learning

摘要

Vertical Federated Learning (VFL) is a distributed machine learning paradigm in which participants train models with vertically partitioned data. Many previous studies have identified backdoor vulnerabilities in VFL systems. However, limited effort has been devoted to developing defenses against such attacks. Unlike centralized machine learning or horizontal FL, VFL poses new challenges for defending against backdoor attacks, particularly because the central server lacks control over the entire model. In this paper, we first explore defenses against backdoor attacks in VFL when the attacker possesses sufficient knowledge of the label information. Specifically, we propose FILTER, a framework for defending against backdoor attacks in VFL to ensure the integrity of VFL systems during training in the presence of malicious participants. To address backdoor risks in VFL, it incorporates two novel filters: an embedding-based filter and a loss-based filter, which effectively identify and remove poisoned samples in later stages of training. Through extensive experiments on five benchmark datasets against four state-of-the-art backdoor attacks, we demonstrate that FILTER significantly reduces the success rate of attacks while maintaining accuracy on clean data close to that of the models trained without such defenses.