General Test-Time Backdoor Detection in Split Neural Network-Based Vertical Federated Learning

As a new distributed machine learning framework, vertical federated learning (VFL) has been widely applied in the industry. However, recent studies have demonstrated that VFL faces serious challenges from backdoor attacks, which significantly hinder its further development. Although a few studies have focused on defending against VFL backdoor attacks, these defenses either do not consider the latest attack methods or show limited effectiveness. Moreover, most existing backdoor defense efforts primarily focus on backdoor attacks in horizontal federated learning (HFL) and centralized learning. Due to the unique architecture of VFL models, these methods cannot be directly applied to backdoor defense in VFL. To mitigate the threat of backdoor attacks in VFL, we propose a general backdoor detection (GBD) scheme for backdoor defense, which detects backdoor samples by analyzing the correlation between backdoor samples and the target label, as well as by leveraging the response differences between clean and backdoor samples. Specifically, we propose two backdoor detection metrics: Class Activation Probability (CAP) and Class Activation Contribution (CAC), which are used to calculate the likelihood of a sample being a backdoor sample. We leverage these two metrics to identify backdoor samples during the inference stage. Evaluation results on both tabular and image datasets show that GBD can detect backdoor samples with high accuracy, demonstrating its effectiveness in backdoor defense. The source code of GBD is available at GitHub: https://github.com/YuanShunJie1/GBD.