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

TL;DR

SecureBoost+ is a scalable, high-performance vertical federated GBDT framework that significantly improves training speed while maintaining accuracy, enabling large-scale data handling with enhanced efficiency.

Contribution

The paper introduces SecureBoost+, which incorporates novel optimizations to significantly accelerate SecureBoost for large-scale data without sacrificing accuracy.

Findings

SecureBoost+ is 6-35x faster than SecureBoost.

It scales to tens of millions of samples and thousands of features.

Maintains the same accuracy as SecureBoost.

Abstract

Gradient boosting decision tree (GBDT) is an ensemble machine learning algorithm, which is widely used in industry, due to its good performance and easy interpretation. Due to the problem of data isolation and the requirement of privacy, many works try to use vertical federated learning to train machine learning models collaboratively with privacy guarantees between different data owners. SecureBoost is one of the most popular vertical federated learning algorithms for GBDT. However, in order to achieve privacy preservation, SecureBoost involves complex training procedures and time-consuming cryptography operations. This causes SecureBoost to be slow to train and does not scale to large scale data. In this work, we propose SecureBoost+, a large-scale and high-performance vertical federated gradient boosting decision tree framework. SecureBoost+ is secure 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 training mechanisms, and multi-classification training optimization. The experimental results show that SecureBoost+ is 6-35x faster than SecureBoost, but with the same accuracy and can be scaled up to tens of millions of data samples and thousands of feature dimensions.