The technique, which builds on recent advances in decentralised machine learning, uses inexpensive pre-programmed micro-computers, making it easy to deploy in hospitals and cheap to scale up.
Due to the need to safeguard patient privacy, hospitals are often limited in the data they can share to support the development of AI algorithms, as once data has been shared it can be difficult to guarantee it remains confidential.
Federated learning was developed in 2017 as a way to train AI algorithms without moving data, and researchers around the world have been working with major technology companies to study how it can be applied in healthcare systems, including in the NHS. However, there has been limited uptake of federated learning in hospitals, in part because its deployment often needs specialist expertise at each hospital taking part in the AI development.
Read the full story on the Oxford Biomedical Research Centre website.