Vertical federated learning (VFL) enables a service provider (i.e., active party) who owns labeled features to collaborate with passive parties who possess auxiliary features to improve model performance. Existing VFL approaches, however, have two major vulnerabilities when passive parties unexpectedly quit in the deployment phase of VFL - severe performance degradation and intellectual property (IP) leakage of the active party's labels. In this paper, we propose \textbf{Party-wise Dropout} to improve the VFL model's robustness against the unexpected exit of passive parties and a defense method called \textbf{DIMIP} to protect the active party's IP in the deployment phase. We evaluate our proposed methods on multiple datasets against different inference attacks. The results show that Party-wise Dropout effectively maintains model performance after the passive party quits, and DIMIP successfully disguises label information from the passive party's feature extractor, thereby mitigating IP leakage.

翻译：纵向联邦学习（VFL）使拥有标注特征的服务提供方（即主动方）能够与持有辅助特征的被动方协作，以提升模型性能。然而，现有VFL方法在部署阶段面临被动方意外退出时的两大关键漏洞——性能严重下降以及主动方标签的知识产权（IP）泄露。本文提出**逐参与方丢弃**方法，以增强VFL模型对被动方意外退出的鲁棒性；同时提出名为**DIMIP**的防御方法，用于在部署阶段保护主动方的知识产权。我们在多个数据集上针对不同推理攻击评估了所提方法。结果表明，逐参与方丢弃能有效维持被动方退出后的模型性能，而DIMIP成功掩盖了被动方特征提取器中的标签信息，从而缓解了知识产权泄露问题。