README.md

Histogram-based Federated Learning for XGBoost

Run automated experiments

Please make sure to finish the preparation steps before running the following steps. To run this example with NVFlare, follow the steps below or this notebook for an interactive experience.

Environment Preparation

Switch to this directory and install additional requirements (suggest to do this inside virtual environment):

python3 -m pip install -r requirements.txt

Run federated experiments with simulator locally

Next, we will use the NVFlare simulator to run FL training automatically.

bash run_experiment_simulator.sh

Run centralized experiments

bash run_experiment_centralized.sh

Run federated experiments in real world

To run in a federated setting, follow Real-World FL to start the overseer, FL servers and FL clients.

You need to download the HIGGS data on each client site. You will also need to install the xgboost on each client site and server site.

You can still generate the data splits and job configs using the scripts provided.

You will need to copy the generated data split file into each client site. You might also need to modify the data_path in the data_site-XXX.json inside the /tmp/nvflare/xgboost_higgs_dataset folder, since each site might save the HIGGS dataset in different places.

Then you can use admin client to submit the job via submit_job command.

Customization

The provided XGBoost executor can be customized using Boost parameters provided in xgb_params argument.

If the parameter change alone is not sufficient and code changes are required, a custom executor can be implemented to make calls to xgboost library directly.

The custom executor can inherit the base class FedXGBHistogramExecutor and overwrite the xgb_train() method.

To use other dataset, can inherit the base class XGBDataLoader and implement that load_data() method.