Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data
This work addresses the under-explored area of federated learning for tabular data, offering a method that improves performance for applications where data privacy and distributed datasets are critical, though it appears incremental as it builds on existing subsampling techniques.
The paper tackles the problem of improving federated learning for tabular data by proposing a histogram-based federated XGBoost using Minimal Variance Sampling, which enhances accuracy and reduces regression error compared to uniform sampling and no sampling, and outperforms centralized XGBoost in half of the cases studied.
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.