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Gradient boosting decision tree (GBDT) is a widely used ensemble algorithm inthe industry. Its vertical federated learning version, SecureBoost, is one ofthe most popular algorithms used in cross-silo privacy-preserving modeling. Asthe area of privacy computation thrives in recent years, demands forlarge-scale and high-performance federated learning have grown dramatically inreal-world applications. In this paper, to fulfill these requirements, wepropose SecureBoost+ that is both novel and improved from the prior workSecureBoost. SecureBoost+ integrates several ciphertext calculationoptimizations and engineering optimizations. The experimental resultsdemonstrate that Secureboost+ has significant performance improvements on largeand high dimensional data sets compared to SecureBoost. It makes effective andefficient large-scale vertical federated learning possible.

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