Toward Privacy-Preserving and Verifiable XGBoost Training for Horizontal Federated Learning

XGBoost, a widely-used machine learning algorithm, has been applied across various fields. To develop a high-quality model, federated learning is employed, allowing participants to keep their data localized while sharing gradients for global model training. However, recent studies have shown that gradients can leak sensitive information, and even when protected, they might still be vulnerable to certain attacks. While several privacy-preserving and verifiable schemes have been proposed to address this issue, they still face limitations in terms of computational and communication costs in certain scenarios. To solve the problem, we propose a privacy-preserving and verifiable XGBoost training scheme for horizontal federated learning. First, we propose a masking with one-time padding protocol (MOTP) to securely aggregate gradients, supporting both online and offline modes for generating random masks. Both approaches reduce the communication costs and improve the training efficiency. Next, we present a linear aggregation verification protocol (LAVP) to ensure the gradient integrity, which avoids complex computations, thereby improving verification efficiency. Building on MOTP and LAVP, we propose a privacy-preserving and verifiable internal node construction algorithm to train a XGBoost model for horizontal data. Detailed security analysis and extensive experiments demonstrate that our scheme can achieve the privacy-preserving, verifiable, lossless, and efficient XGBoost training for horizontally partitioned data.