1 McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Canada.
2 Epione Research Team, Inria Center of Université Côte d'Azur, Sophia Antipolis, France.
3 Department of Computer Science and Software Engineering, Gina Cody School of Engineering and Computer Science, Concordia University.
4 Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
5 Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands.
6 SED Group, Inria Center of Université Côte d'Azur, Sophia Antipolis, France.
7 Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, Canada.
8 Toronto Western Hospital, University of Toronto, Toronto, Canada.
9 Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
10 Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands.
11 Centre for Analytics, Ontario Brain Institute, Toronto, Canada.
12 Département de radiologie, radio-oncologie, et médecine nucléaire, Université de Montréal.

Despite growing understanding of the benefits of having Findable, Accessible, Interoperable, and Reusable (FAIR) data, many datasets still cannot be shared. Federated analysis methods can enable multisite studies that do not require the sharing of participant-level information. However, there are many practical hurdles that prevent the large-scale adoption of federated methods. We discuss challenges related to cross-site data preparation for federated learning, present solutions offered by recent neuroinformatics projects, and showcase an example of tool integration applied to neurodegenerative disease data.