668b8a0ea4
* Switch from rabit to the collective communicator * fix size_t specialization * really fix size_t * try again * add include * more include * fix lint errors * remove rabit includes * fix pylint error * return dict from communicator context * fix communicator shutdown * fix dask test * reset communicator mocklist * fix distributed tests * do not save device communicator * fix jvm gpu tests * add python test for federated communicator * Update gputreeshap submodule Co-authored-by: Hyunsu Philip Cho <chohyu01@cs.washington.edu>
Experimental Support of Federated XGBoost using NVFlare
This directory contains a demo of Federated Learning using NVFlare.
Training with CPU only
To run the demo, first build XGBoost with the federated learning plugin enabled (see the README).
Install NVFlare (note that currently NVFlare only supports Python 3.8; for NVFlare 2.1.2 we also need to pin the protobuf package to 3.20.x to avoid protoc errors):
pip install nvflare protobuf==3.20.1
Prepare the data:
./prepare_data.sh
Start the NVFlare federated server:
./poc/server/startup/start.sh
In another terminal, start the first worker:
./poc/site-1/startup/start.sh
And the second worker:
./poc/site-2/startup/start.sh
Then start the admin CLI, using admin/admin as username/password:
./poc/admin/startup/fl_admin.sh
In the admin CLI, run the following command:
submit_job hello-xgboost
Once the training finishes, the model file should be written into
./poc/site-1/run_1/test.model.json and ./poc/site-2/run_1/test.model.json
respectively.
Finally, shutdown everything from the admin CLI:
shutdown client
shutdown server
Training with GPUs
To demo with Federated Learning using GPUs, make sure your machine has at least 2 GPUs. Build XGBoost with the federated learning plugin enabled along with CUDA, but with NCCL turned off (see the README).
Modify config/config_fed_client.json and set use_gpus to true, then repeat the steps
above.