xgboost/demo/nvflare/horizontal

Experimental Support of Horizontal Federated XGBoost using NVFlare

This directory contains a demo of Horizontal 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):

pip install nvflare

Prepare the data:

./prepare_data.sh

Start the NVFlare federated server:

/tmp/nvflare/poc/server/startup/start.sh

In another terminal, start the first worker:

/tmp/nvflare/poc/site-1/startup/start.sh

And the second worker:

/tmp/nvflare/poc/site-2/startup/start.sh

Then start the admin CLI:

/tmp/nvflare/poc/admin/startup/fl_admin.sh

In the admin CLI, run the following command:

submit_job horizontal-xgboost

Make a note of the job id:

Submitted job: 28309e77-a7c5-45e6-b2bc-c2e3655122d8

On both workers, you should see train and eval losses printed:

[10:45:41] [0]	eval-logloss:0.22646	train-logloss:0.23316
[10:45:41] [1]	eval-logloss:0.13776	train-logloss:0.13654
[10:45:41] [2]	eval-logloss:0.08036	train-logloss:0.08243
[10:45:41] [3]	eval-logloss:0.05830	train-logloss:0.05645
[10:45:41] [4]	eval-logloss:0.03825	train-logloss:0.04148
[10:45:41] [5]	eval-logloss:0.02660	train-logloss:0.02958
[10:45:41] [6]	eval-logloss:0.01386	train-logloss:0.01918
[10:45:41] [7]	eval-logloss:0.01018	train-logloss:0.01331
[10:45:41] [8]	eval-logloss:0.00847	train-logloss:0.01112
[10:45:41] [9]	eval-logloss:0.00691	train-logloss:0.00662
[10:45:41] [10]	eval-logloss:0.00543	train-logloss:0.00503
[10:45:41] [11]	eval-logloss:0.00445	train-logloss:0.00420
[10:45:41] [12]	eval-logloss:0.00336	train-logloss:0.00355
[10:45:41] [13]	eval-logloss:0.00277	train-logloss:0.00280
[10:45:41] [14]	eval-logloss:0.00252	train-logloss:0.00244
[10:45:41] [15]	eval-logloss:0.00177	train-logloss:0.00193
[10:45:41] [16]	eval-logloss:0.00156	train-logloss:0.00161
[10:45:41] [17]	eval-logloss:0.00135	train-logloss:0.00142
[10:45:41] [18]	eval-logloss:0.00123	train-logloss:0.00125
[10:45:41] [19]	eval-logloss:0.00106	train-logloss:0.00107

Once the training finishes, the model file should be written into /tmp/nvlfare/poc/site-1/${job_id}/test.model.json and /tmp/nvflare/poc/site-2/${job_id}/test.model.json respectively, where job_id is the UUID printed out when we ran submit_job.

Finally, shutdown everything from the admin CLI, using admin as password:

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 (see the README).

Modify ../config/config_fed_client.json and set use_gpus to true, then repeat the steps above.