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.

翻译：联邦学习是一种分布式的机器学习框架,它使多方能够进行合作培训,同时确保数据隐私。由于传统隐私保护方法产生的高昂成本,对最先进的树木促进框架XGBoost(最先进的树木促进框架)的实用适应性仍然有限。为了解决这个问题,我们提出了两种具有隐私保障的联邦式XGBoost(FedXGBost-SMM)和FedXGBoost-LDP(FedXGBoost-SMM)的变体。我们的第一个协议FedXGBost(FedXGBoost)-SMM(FedXGBoost-SMM)采用了强化的安全矩阵倍增法,以保护隐私,其准确性不降低加密技术的间接费用。独立开发的第二议定书FedXGBost-LDP(FedXGBost-LDP)是用超声干扰本地差异隐私的噪音设计的,并在现实世界和合成数据集上进行了经验评估。