FedXGBoost: Privacy-Preserving XGBoost for Federated Learning
This work addresses privacy-preserving machine learning for distributed parties, offering incremental improvements in efficiency for federated XGBoost.
The paper tackles the challenge of adapting XGBoost to federated learning with privacy guarantees, proposing two protocols (FedXGBoost-SMM and FedXGBoost-LDP) that achieve lossless accuracy and lower overhead compared to encryption-based methods, as evaluated on real-world and synthetic datasets.
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.