In response to legislation mandating companies to honor the \textit{right to be forgotten} by erasing user data, it has become imperative to enable data removal in Vertical Federated Learning (VFL) where multiple parties provide private features for model training. In VFL, data removal, i.e., \textit{machine unlearning}, often requires removing specific features across all samples under privacy guarentee in federated learning. To address this challenge, we propose \methname, a novel Gradient Boosting Decision Tree (GBDT) framework that effectively enables both \textit{instance unlearning} and \textit{feature unlearning} without the need for retraining from scratch. Leveraging a robust GBDT structure, we enable effective data deletion while reducing degradation of model performance. Extensive experimental results on popular datasets demonstrate that our method achieves superior model utility and forgetfulness compared to \textit{state-of-the-art} methods. To our best knowledge, this is the first work that investigates machine unlearning in VFL scenarios.

翻译：为响应立法要求企业通过删除用户数据来践行“被遗忘权”，在多方提供私有特征联合训练模型的纵向联邦学习中实现数据删除已成为当务之急。在纵向联邦学习中，数据删除（即机器遗忘）通常需要在隐私保护条件下跨所有样本移除特定特征。为解决这一挑战，本文提出SecureCut——一种新颖的梯度提升决策树框架，该框架无需从头重新训练即可有效实现实例遗忘与特征遗忘。通过利用稳健的梯度提升决策树结构，我们在保持模型性能降幅最小的前提下实现了有效数据删除。在主流数据集上的大量实验结果表明，与现有最优方法相比，本方法在模型效用与遗忘能力方面均表现更优。据我们所知，这是首个针对纵向联邦学习场景中机器遗忘问题的研究工作。