Artificial intelligence is rapidly transforming our society. Machine learning models will be components in nearly every digital system we use. For this reason, there is an urgent need for methods and software that allows for development of state-of-the art ML models while protecting the integrity of data owners .

In this project we work on algorithms, scalable implementations, and applications of Federated Learning (FedML) – an approach to training ML models while keeping input data privacy of data owners.

This project is a collaboration with our spin-out company Scaleout Systems. Together with Scaleout we develop the open source framework FEDn – a framework for privacy-preserving AI using a highly scalable implementation of federated learning.


We are collaborating with RaySearch Laboratories on applications of FedML in radiotherapy. This is a project in the eSSENCE collaboration on eScience.