Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data
This addresses privacy and data-sharing limitations in medical research, enabling collaborative analysis across institutions, though it is incremental as federated learning is an established concept applied to a specific domain.
The authors tackled the problem of analyzing distributed medical databases without sharing individual data due to privacy concerns by proposing a federated learning framework, which they applied to brain structural data from multiple cohorts like ADNI and UK Biobank, demonstrating its potential for secure meta-analysis.
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