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In response to legislation mandating companies to honor the \textit{right tobe forgotten} by erasing user data, it has become imperative to enable dataremoval in Vertical Federated Learning (VFL) where multiple parties provideprivate features for model training. In VFL, data removal, i.e.,\textit{machine unlearning}, often requires removing specific features acrossall samples under privacy guarentee in federated learning. To address thischallenge, 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 whilereducing degradation of model performance. Extensive experimental results onpopular datasets demonstrate that our method achieves superior model utilityand forgetfulness compared to \textit{state-of-the-art} methods. To our bestknowledge, this is the first work that investigates machine unlearning in VFLscenarios.

SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning