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FederBoost's Federated Gradient Boosting Decision Tree Algorithm, Federated enabled Membership Inference

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Jaap-Meerhof/Federated_XGBoost_Python

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Federated Membership Inference on Federboost

This package implements 'Federboost' from Tian et al Which is a horizontally federated Gradient Boosted Decision Tree algorithm. With the regularisation terms of XGBoost. This is not a 1 to 1 conversion from XGBoost to python, the models perform remarkably close when regularisation parameters are used, but they differ more when it is not used.

This is a multi-class classification implementation that uses softmax. Also different types of Membership Inference Attack can be tested, both using federated information or without.

Get the paper here

TODO:

  • Clear up for other researchers
    • code cleanup & explanation
  • Change naming from old SFXGBoost to FXGBoost

Usage: install conda environment:

conda env create -f environment.yml

Activate your environment:

conda activate fedxgboost_mpi

build and run the experiments using:

pip install . && mpiexec -np 3 python tests/main.py (experiment number 1 to 3)

here -np 3 implies 1 server and two participants in the federated network. for this you will have to change the POSSIBLE_PATHS parameter for the healthcare dataset.

or use "run.sh":

./run.sh myexperimentname (experiment number 1 to 3)

Tian, Z., Zhang, R., Hou, X., Liu, J., & Ren, K. (2020). Federboost: Private federated learning for gbdt. arXiv preprint arXiv:2011.02796.


Made in collaboration with the RIVM (Dutch National Institute for Public Health and the Environment) for my master thesis at the University of Twente.

Made in collaboration with the RIVM (Dutch National Institute for Public Health and the Environment)

check https://jaap-meerhof.github.io for my contact information