A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks
This provides a comparative benchmark for federated learning on tabular data, addressing a gap for practitioners but is incremental as it applies existing methods to new data.
The study benchmarked federated tree-based models (TBMs) and deep neural networks (DNNs) on tabular data, finding that federated XGBoost outperformed all other models across 10 datasets and varying data partitions, with TBMs generally beating DNNs even with many clients.
Federated Learning (FL) has lately gained traction as it addresses how machine learning models train on distributed datasets. FL was designed for parametric models, namely Deep Neural Networks (DNNs).Thus, it has shown promise on image and text tasks. However, FL for tabular data has received little attention. Tree-Based Models (TBMs) have been considered to perform better on tabular data and they are starting to see FL integrations. In this study, we benchmark federated TBMs and DNNs for horizontal FL, with varying data partitions, on 10 well-known tabular datasets. Our novel benchmark results indicates that current federated boosted TBMs perform better than federated DNNs in different data partitions. Furthermore, a federated XGBoost outperforms all other models. Lastly, we find that federated TBMs perform better than federated parametric models, even when increasing the number of clients significantly.