Select the tutorial that fits your needs. To facilitate the learning process of Federated Learning, one tutorial with a UI-based approach and one tutorial with an API calling approach for multiple frameworks and data sets is provided. The results of either are the same. All UI-based tutorials demonstrate how to create the Federated Learning experiment in a low-code environment. All API-based tutorials use two sample notebooks with Python scripts to demonstrate how to build and train the experiment.
Tensorflow
These hands-on tutorials teach you how to create a Federated Learning experiment step by step. These tutorials use the MNIST data set to demonstrate how different parties can contribute data to train a model to recognize handwriting. You can choose between a UI-based or API version of the tutorial.
XGBoost
This is a tutorial for Federated Learning that teaches you how to create an experiment step by step with an income in the XGBoost framework. The tutorial demonstrates how different parties can contribute data to train a model about adult incomes.
Homomorphic encryption
This is a tutorial for Federated Learning that teaches you how to use the advanced method of homomorphic encryption step by step.
Parent topic: IBM Federated learning