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

TL;DR

This paper introduces a histogram-based federated XGBoost model utilizing Minimal Variance Sampling, which improves accuracy and regression error in federated tabular data scenarios, outperforming uniform sampling and sometimes centralized models.

Contribution

It presents a novel federated XGBoost algorithm with Minimal Variance Sampling that enhances performance on tabular data in federated learning settings.

Findings

MVS improves federated XGBoost accuracy and regression error.

MVS outperforms uniform sampling and no sampling.

Federated XGBoost with MVS outperforms centralized XGBoost in half of the cases.

Abstract

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.