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.
@article{arxiv.2106.10662,
title = {FedXGBoost: Privacy-Preserving XGBoost for Federated Learning},
author = {Nhan Khanh Le and Yang Liu and Quang Minh Nguyen and Qingchen Liu and Fangzhou Liu and Quanwei Cai and Sandra Hirche},
journal= {arXiv preprint arXiv:2106.10662},
year = {2021}
}