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Gradient boosting decision tree (GBDT) is an ensemble machine learningalgorithm, which is widely used in industry, due to its good performance andeasy interpretation. Due to the problem of data isolation and the requirementof privacy, many works try to use vertical federated learning to train machinelearning models collaboratively with privacy guarantees between different dataowners. SecureBoost is one of the most popular vertical federated learningalgorithms for GBDT. However, in order to achieve privacy preservation,SecureBoost involves complex training procedures and time-consumingcryptography operations. This causes SecureBoost to be slow to train and doesnot scale to large scale data.  In this work, we propose SecureBoost+, a large-scale and high-performancevertical federated gradient boosting decision tree framework. SecureBoost+ issecure in the semi-honest model, which is the same as SecureBoost. SecureBoost+can be scaled up to tens of millions of data samples easily. SecureBoost+achieves high performance through several novel optimizations for SecureBoost,including ciphertext operation optimization, the introduction of new trainingmechanisms, and multi-classification training optimization. The experimentalresults show that SecureBoost+ is 6-35x faster than SecureBoost, but with thesame accuracy and can be scaled up to tens of millions of data samples andthousands of feature dimensions.

SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree