FedXGBoost: Privacy-Preserving XGBoost for Federated Learning

• URL: http://arxiv.org/abs/2106.10662v2
• Date: Tue, 22 Jun 2021 14:50:42 GMT
• Title: FedXGBoost: Privacy-Preserving XGBoost for Federated Learning
• Authors: Nhan Khanh Le and Yang Liu and Quang Minh Nguyen and Qingchen Liu and Fangzhou Liu and Quanwei Cai and Sandra Hirche
• Abstract summary: Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy. We propose two variants of federated XGBoost with privacy guarantee: FedXGBoost-SMM and FedXGBoost-LDP.
• Score: 10.304484601250948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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