# SecureGBM: Secure Multi-Party Gradient Boosting

arXiv: https://arxiv.org/abs/1911.11997 · HTML: https://mlsanity.com/paper/1911.11997/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 19 of 19)

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- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning](https://mlsanity.com/paper/2105.09540/) (2021)
- [An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization](https://mlsanity.com/paper/2105.05717/) (2021)
- [Federated Boosted Decision Trees with Differential Privacy](https://mlsanity.com/paper/2210.02910/) (2022)
- [VFLAIR: A Research Library and Benchmark for Vertical Federated Learning](https://mlsanity.com/paper/2310.09827/) (2023)
- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps](https://mlsanity.com/paper/2111.09445/) (2021)
- [Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost](https://mlsanity.com/paper/2112.04261/) (2021)
- [FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging](https://mlsanity.com/paper/2204.00976/) (2022)
- [SoK: Privacy-Preserving Collaborative Tree-based Model Learning](https://mlsanity.com/paper/2103.08987/) (2021)
- [Federated XGBoost on Sample-Wise Non-IID Data](https://mlsanity.com/paper/2209.01340/) (2022)
- [Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network](https://mlsanity.com/paper/2404.09443/) (2024)
- [Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data](https://mlsanity.com/paper/2405.02067/) (2024)
- [Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy](https://mlsanity.com/paper/2602.10100/) (2026)

## Abstract (arXiv)

Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation model based on semi-homomorphic encryption, where every involved party can jointly obtain a shared Gradient Boosting machines model while protecting their own data from the potential privacy leakage and inferential identification. More specific, our work focused on a specific "dual--party" secure learning scenario based on two parties -- both party own an unique view (i.e., attributes or features) to the sample group of samples while only one party owns the labels. In such scenario, feature and label data are not allowed to share with others. To achieve the above goal, we firstly extent -- LightGBM -- a well known implementation of tree-based GBM through covering its key operations for training and inference with SEAL homomorphic encryption schemes. However, the performance of such re-implementation is significantly bottle-necked by the explosive inflation of the communication payloads, based on ciphertexts subject to the increasing length of plaintexts. In this way, we then proposed to use stochastic approximation techniques to reduced the communication payloads while accelerating the overall training procedure in a statistical manner. Our experiments using the real-world data showed that SecureGBM can well secure the communication and computation of LightGBM training and inference procedures for the both parties while only losing less than 3% AUC, using the same number of iterations for gradient boosting, on a wide range of benchmark datasets.