  At this moment, databanks worldwide contain brain images of previously
unimaginable numbers. Combined with developments in data science, these massive
data provide the potential to better understand the genetic underpinnings of
brain diseases. However, different datasets, which are stored at different
institutions, cannot always be shared directly due to privacy and legal
concerns, thus limiting the full exploitation of big data in the study of brain
disorders. Here we propose a federated learning framework for securely
accessing and meta-analyzing any biomedical data without sharing individual
information. We illustrate our framework by investigating brain structural
relationships across diseases and clinical cohorts. The framework is first
tested on synthetic data and then applied to multi-centric, multi-database
studies including ADNI, PPMI, MIRIAD and UK Biobank, showing the potential of
the approach for further applications in distributed analysis of multi-centric
cohorts
