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

arXiv:2405.02067v1 Announce Type: new
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 …

abstract arxiv attention cs.lg data federated learning networks neural networks research sampling tabular tabular data tree type variance while xgboost