# An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization

arXiv: https://arxiv.org/abs/2105.05717 · HTML: https://mlsanity.com/paper/2105.05717/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 10 of 10)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Federated Forest](https://mlsanity.com/paper/1905.10053/) (2019)
- [Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data](https://mlsanity.com/paper/2008.06197/) (2020)
- [SecureGBM: Secure Multi-Party Gradient Boosting](https://mlsanity.com/paper/1911.11997/) (2019)
- [Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing](https://mlsanity.com/paper/1907.10218/) (2019)
- [Revocable Federated Learning: A Benchmark of Federated Forest](https://mlsanity.com/paper/1911.03242/) (2019)
- [Cloud-based Federated Boosting for Mobile Crowdsensing](https://mlsanity.com/paper/2005.05304/) (2020)

## Built on by (most cited first, 9 of 9)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [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)
- [Tree-based Models for Vertical Federated Learning: A Survey](https://mlsanity.com/paper/2504.02285/) (2025)
- [Privacy-Preserving Distributed Optimization and Learning](https://mlsanity.com/paper/2403.00157/) (2024)
- [Blind Federated Learning without initial model](https://mlsanity.com/paper/2404.16180/) (2024)
- [Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning](https://mlsanity.com/paper/2405.20761/) (2024)
- [Concurrent vertical and horizontal federated learning with fuzzy cognitive maps](https://mlsanity.com/paper/2412.12844/) (2024)

## Abstract (arXiv)

XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency. Targeting at data isolation issues in the big data problems, it is crucial to deploy a secure and efficient federated XGBoost (FedXGB) model. Existing FedXGB models either have data leakage issues or are only applicable to the two-party setting with heavy communication and computation overheads. In this paper, a lossless multi-party federated XGB learning framework is proposed with a security guarantee, which reshapes the XGBoost's split criterion calculation process under a secret sharing setting and solves the leaf weight calculation problem by leveraging distributed optimization. Remarkably, a thorough analysis of model security is provided as well, and multiple numerical results showcase the superiority of the proposed FedXGB compared with the state-of-the-art models on benchmark datasets.