In vertical federated learning (VFL), commercial entities collaboratively
train a model while preserving data privacy. However, a malicious participant's
poisoning attack may degrade the performance of this collaborative model. The
main challenge in achieving the poisoning attack is the absence of access to
the server-side top model, leaving the malicious participant without a clear
target model. To address this challenge, we introduce an innovative end-to-end
poisoning framework P-GAN. Specifically, the malicious participant initially
employs semi-supervised learning to train a surrogate target model.
Subsequently, this participant employs a GAN-based method to produce
adversarial perturbations to degrade the surrogate target model's performance.
Finally, the generator is obtained and tailored for VFL poisoning. Besides, we
develop an anomaly detection algorithm based on a deep auto-encoder (DAE),
offering a robust defense mechanism to VFL scenarios. Through extensive
experiments, we evaluate the efficacy of P-GAN and DAE, and further analyze the
factors that influence their performance.

在垂直联邦学习中，通过保护数据隐私，商业实体共同训练一个模型。然而，恶意参与者的毒化攻击可能会降低这个合作模型的性能。为了解决这个挑战，本文引入了一种创新的端到端毒化框架 P-GAN。同时，我们还开发了一种基于深度自编码器（DAE）的异常检测算法，为垂直联邦学习情景提供了强大的防御机制。通过大量实验，我们评估了 P-GAN 和 DAE 的效能，并进一步分析了影响它们性能的因素。