Federated XGBoost in Horizontal Setting (PyTorch)#

Open in Colab (or open the Jupyter Notebook)

This example demonstrates a federated XGBoost using Flower with PyTorch. This is a novel method to conduct federated XGBoost in the horizontal setting. It differs from the previous methods in the following ways:

  • We aggregate and conduct federated learning on client tree’s prediction outcomes by sending clients’ built XGBoost trees to the server and then sharing to the clients.

  • The exchange of privacy-sensitive information (gradients) is not needed.

  • The model is a CNN with 1D convolution kernel size = the number of XGBoost trees in the client tree ensembles.

  • Using 1D convolution, we make the tree learning rate (a hyperparameter of XGBoost) learnable.

Project Setup#

This implementation can be easily run in Google Colab with the button at the top of the README or as a standalone Jupyter notebook, it will automatically download and extract the example data inside a dataset folder and binary_classification and regression sub-folders.

Datasets#

This implementation supports both binary classification and regression datasets in SVM light format, loaded from (LIBSVM Data). Simply download the dataset files from the website and put them in the folder location indicated above.