# FedXGBoost: Privacy-Preserving XGBoost for Federated Learning

arXiv: https://arxiv.org/abs/2106.10662 · HTML: https://mlsanity.com/paper/2106.10662/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 20 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Practical Federated Gradient Boosting Decision Trees](https://mlsanity.com/paper/1911.04206/) (2019)
- [Collecting and Analyzing Multidimensional Data with Local Differential Privacy](https://mlsanity.com/paper/1907.00782/) (2019)
- [Privacy-Preserving Gradient Boosting Decision Trees](https://mlsanity.com/paper/1911.04209/) (2019)
- [Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation](https://mlsanity.com/paper/1305.6000/) (2013)
- [Optimal Quantile Approximation in Streams](https://mlsanity.com/paper/1603.05346/) (2016)

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

- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals](https://mlsanity.com/paper/2208.10993/) (2022)
- [Federated Boosted Decision Trees with Differential Privacy](https://mlsanity.com/paper/2210.02910/) (2022)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning](https://mlsanity.com/paper/2206.03852/) (2022)
- [Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case](https://mlsanity.com/paper/2405.02060/) (2024)
- [Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review](https://mlsanity.com/paper/2405.13832/) (2024)
- [FedLLM-Align: Feature Extraction From Heterogeneous Clients](https://mlsanity.com/paper/2510.00065/) (2025)

## Abstract (arXiv)

Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy. Practical adaptation of XGBoost, the state-of-the-art tree boosting framework, to federated learning remains limited due to high cost incurred by conventional privacy-preserving methods. To address the problem, we propose two variants of federated XGBoost with privacy guarantee: FedXGBoost-SMM and FedXGBoost-LDP. Our first protocol FedXGBoost-SMM deploys enhanced secure matrix multiplication method to preserve privacy with lossless accuracy and lower overhead than encryption-based techniques. Developed independently, the second protocol FedXGBoost-LDP is heuristically designed with noise perturbation for local differential privacy, and empirically evaluated on real-world and synthetic datasets.