  Federated learning (FL) is an emerging privacy preserving machine learning
protocol that allows multiple devices to collaboratively train a shared global
model without revealing their private local data. Non-parametric models like
gradient boosting decision trees (GBDT) have been commonly used in FL for
vertically partitioned data. However, all these studies assume that all the
data labels are stored on only one client, which may be unrealistic for
real-world applications. Therefore, in this work, we propose a secure vertical
FL framework, named PIVODL, to train GBDT with data labels distributed on
multiple devices. Both homomorphic encryption and differential privacy are
adopted to prevent label information from being leaked through transmitted
gradients and leaf values. Our experimental results show that both information
leakage and model performance degradation of the proposed PIVODL are
negligible.
