DaSCI has created a repository that contains information that promotes lifelong learning and that is highly topical recently.
When data privacy is imposed as a necessity, Federated Learning (FL) emerges as a relevant Artificial Intelligence field for developing machine learning models in a distributed and decentralized environment reducing the exposure of sensitive data. This repository contains some Python notebooks as a tutorial on Federated Learning by systematically examining the functionalities provided by existing software frameworks for FL deployment. It includes examples of how to design an FL scenario and experiments, showing how to solve problems either at Horizontal or Vertical Federated Learning scenarios. The use cases are implemented using TensorFlow Federated, Flower, and FATE frameworks.
GitHub repository: https://github.com/ari-dasci/S-TutorialFL
Contact: Victoria Luzón