  Federated learning is the distributed machine learning framework that enables
collaborative training across multiple parties while ensuring data privacy.
Practical adaptation of XGBoost, the state-of-the-art tree boosting framework,
to federated learning remains limited due to high cost incurred by conventional
privacy-preserving methods. To address the problem, we propose two variants of
federated XGBoost with privacy guarantee: FedXGBoost-SMM and FedXGBoost-LDP.
Our first protocol FedXGBoost-SMM deploys enhanced secure matrix multiplication
method to preserve privacy with lossless accuracy and lower overhead than
encryption-based techniques. Developed independently, the second protocol
FedXGBoost-LDP is heuristically designed with noise perturbation for local
differential privacy, and empirically evaluated on real-world and synthetic
datasets.
