SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning [article]

Weijing Chen, Guoqiang Ma, Tao Fan, Yan Kang, Qian Xu, Qiang Yang
2021 arXiv   pre-print
Gradient boosting decision tree (GBDT) is a widely used ensemble algorithm in the industry. Its vertical federated learning version, SecureBoost, is one of the most popular algorithms used in cross-silo privacy-preserving modeling. As the area of privacy computation thrives in recent years, demands for large-scale and high-performance federated learning have grown dramatically in real-world applications. In this paper, to fulfill these requirements, we propose SecureBoost+ that is both novel
more » ... improved from the prior work SecureBoost. SecureBoost+ integrates several ciphertext calculation optimizations and engineering optimizations. The experimental results demonstrate that Secureboost+ has significant performance improvements on large and high dimensional data sets compared to SecureBoost. It makes effective and efficient large-scale vertical federated learning possible.
arXiv:2110.10927v3 fatcat:ovyk374r65gaxkopvizhawrnzm