Flower Tutorial | Federated Learning Quickstart with Flower and XGBoost

This tutorial will elaborate how to train a federated XGBoost model with Flower. It is aimed at an audience already somewhat familiar with Federated Learning. If that's not the case for you, be sure to subscribe to our channel because we're planning on releasing a lot more beginner friendly content in the future! In it we quickly explain what is XGBoost. Then we build a simple code example to demonstrate how to use Flower to train XGBoost models in federated fashion with bagging aggregation. Your feedback is very important to us, so please tell us in the comments below what kind of video you would like to see next! The full code of this example can be found here: https://github.com/adap/flower/tree/m... And the associated doc page: https://flower.ai/docs/framework/tuto... Join the Flower community on Slack: https://flower.ai/join-slack/ And be sure to give us a star on GitHub: https://github.com/adap/flower 0:00 Intro 0:22 Environment setup 1:22 Client code: data partitioning and hyper-parameters selection 6:38 Client code: XGBoost client 12:15 Server code 14:23 Bagging strategy 15:58 Running experiment 17:12 Outro