This tutorial will go over how you can go from centralized Machine Learning to Federated Learning quickly using Flower and TensorFlow.
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!
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!
A similar code example can be found here: https://github.com/adap/flower/tree/main/examples/quickstart-tensorflow
And the associated doc page:
https://flower.ai/docs/framework/quickstart-tensorflow.html
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 Introduction to Flower Tutorials
0:22 Dependencies
0:41 Writing the TensorFlow pipeline
3:22 Federating our TensorFlow pipeline
4:04 fit function for the client
6:07 evaluate function for the client
7:35 get_parameters function for the client
9:09 Writing the Flower Server
10:41 Starting the Federated Learning
12:17 Adding a custom server config
13:11 Analysing the results
14:51 Custom strategies
