  The ability to combine data across scanners and studies is vital for
neuroimaging, to increase both statistical power and the representation of
biological variability. However, combining datasets across sites leads to two
challenges: first, an increase in undesirable non-biological variance due to
scanner and acquisition differences - the harmonisation problem - and second,
data privacy concerns due to the inherently personal nature of medical imaging
data, meaning that sharing them across sites may risk violation of privacy
laws. To overcome these restrictions, we propose FedHarmony: a harmonisation
framework operating in the federated learning paradigm. We show that to remove
the scanner-specific effects, we only need to share the mean and standard
deviation of the learned features, helping to protect individual subjects'
privacy. We demonstrate our approach across a range of realistic data
scenarios, using real multi-site data from the ABIDE dataset, thus showing the
potential utility of our method for MRI harmonisation across studies. Our code
is available at https://github.com/nkdinsdale/FedHarmony.
