SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866929391009792000 |
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| author | Fan, Tao Chen, Weijing Ma, Guoqiang Kang, Yan Fan, Lixin Yang, Qiang |
| author_facet | Fan, Tao Chen, Weijing Ma, Guoqiang Kang, Yan Fan, Lixin Yang, Qiang |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2110_10927 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree Fan, Tao Chen, Weijing Ma, Guoqiang Kang, Yan Fan, Lixin Yang, Qiang Machine Learning Artificial Intelligence 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. |
| title | SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2110.10927 |