  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.
