  XGBoost is one of the most widely used machine learning models in the
industry due to its superior learning accuracy and efficiency. Targeting at
data isolation issues in the big data problems, it is crucial to deploy a
secure and efficient federated XGBoost (FedXGB) model. Existing FedXGB models
either have data leakage issues or are only applicable to the two-party setting
with heavy communication and computation overheads. In this paper, a lossless
multi-party federated XGB learning framework is proposed with a security
guarantee, which reshapes the XGBoost's split criterion calculation process
under a secret sharing setting and solves the leaf weight calculation problem
by leveraging distributed optimization. Remarkably, a thorough analysis of
model security is provided as well, and multiple numerical results showcase the
superiority of the proposed FedXGB compared with the state-of-the-art models on
benchmark datasets.
