xgboost/demo/nvflare/README.md

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# Experimental Support of Federated XGBoost using NVFlare
This directory contains a demo of Federated Learning using
[NVFlare](https://nvidia.github.io/NVFlare/).
## Training with CPU only
To run the demo, first build XGBoost with the federated learning plugin enabled (see the
[README](../../plugin/federated/README.md)).
Install NVFlare (note that currently NVFlare only supports Python 3.8):
```shell
pip install nvflare
```
Prepare the data:
```shell
./prepare_data.sh
```
Start the NVFlare federated server:
```shell
/tmp/nvflare/poc/server/startup/start.sh
```
In another terminal, start the first worker:
```shell
/tmp/nvflare/poc/site-1/startup/start.sh
```
And the second worker:
```shell
/tmp/nvflare/poc/site-2/startup/start.sh
```
Then start the admin CLI:
```shell
/tmp/nvflare/poc/admin/startup/fl_admin.sh
```
In the admin CLI, run the following command:
```shell
submit_job hello-xgboost
```
Once the training finishes, the model file should be written into
`/tmp/nvlfare/poc/site-1/run_1/test.model.json` and `/tmp/nvflare/poc/site-2/run_1/test.model.json`
respectively.
Finally, shutdown everything from the admin CLI, using `admin` as password:
```shell
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](../../plugin/federated/README.md)).
Modify `config/config_fed_client.json` and set `use_gpus` to `true`, then repeat the steps
above.