Demo of federated learning using NVFlare (#7879)

Co-authored-by: jiamingy <jm.yuan@outlook.com>

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/).
+
+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
+./poc/server/startup/start.sh
+```
+
+In another terminal, start the first worker:
+```shell
+./poc/site-1/startup/start.sh
+```
+
+And the second worker:
+```shell
+./poc/site-2/startup/start.sh
+```
+
+Then start the admin CLI, using `admin/admin` as username/password:
+```shell
+./poc/admin/startup/fl_admin.sh
+```
+
+In the admin CLI, run the following commands:
+```shell
+upload_app hello-xgboost
+set_run_number 1
+deploy_app hello-xgboost all
+start_app all
+```
+
+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:
+```shell
+shutdown client
+shutdown server
+```