# Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data

arXiv: https://arxiv.org/abs/2405.02067 · HTML: https://mlsanity.com/paper/2405.02067/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 20 of 20)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Privacy Preserving Vertical Federated Learning for Tree-based Models](https://mlsanity.com/paper/2008.06170/) (2020)
- [A Comparative Analysis of XGBoost](https://mlsanity.com/paper/1911.01914/) (2019)
- [NVIDIA FLARE: Federated Learning from Simulation to Real-World](https://mlsanity.com/paper/2210.13291/) (2022)
- [Federated Forest](https://mlsanity.com/paper/1905.10053/) (2019)
- [FederBoost: Private Federated Learning for GBDT](https://mlsanity.com/paper/2011.02796/) (2020)
- [Optimal Importance Sampling for Federated Learning](https://mlsanity.com/paper/2010.13600/) (2020)
- [SecureGBM: Secure Multi-Party Gradient Boosting](https://mlsanity.com/paper/1911.11997/) (2019)
- [The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost](https://mlsanity.com/paper/1907.07157/) (2019)
- [Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing](https://mlsanity.com/paper/1907.10218/) (2019)
- [Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning](https://mlsanity.com/paper/2012.06670/) (2020)
- [Minimal Variance Sampling in Stochastic Gradient Boosting](https://mlsanity.com/paper/1910.13204/) (2019)
- [Federated XGBoost on Sample-Wise Non-IID Data](https://mlsanity.com/paper/2209.01340/) (2022)
- [Out-of-Core GPU Gradient Boosting](https://mlsanity.com/paper/2005.09148/) (2020)

## Built on by (most cited first, 1 of 1)

- [FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring](https://mlsanity.com/paper/2603.06224/) (2026)

## Abstract (arXiv)

Federated Learning (FL) has gained considerable traction, yet, for tabular data, FL has received less attention. Most FL research has focused on Neural Networks while Tree-Based Models (TBMs) such as XGBoost have historically performed better on tabular data. It has been shown that subsampling of training data when building trees can improve performance but it is an open problem whether such subsampling can improve performance in FL. In this paper, we evaluate a histogram-based federated XGBoost that uses Minimal Variance Sampling (MVS). We demonstrate the underlying algorithm and show that our model using MVS can improve performance in terms of accuracy and regression error in a federated setting. In our evaluation, our model using MVS performs better than uniform (random) sampling and no sampling at all. It achieves both outstanding local and global performance on a new set of federated tabular datasets. Federated XGBoost using MVS also outperforms centralized XGBoost in half of the studied cases.