Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

TL;DR

This paper presents a federated learning framework that enables secure, privacy-preserving meta-analysis of large-scale brain imaging data across multiple institutions, facilitating collaborative research without data sharing.

Contribution

The paper introduces a novel federated learning approach tailored for distributed medical databases, allowing multi-center brain data analysis while maintaining privacy.

Findings

Framework successfully tested on synthetic data

Applied to multi-centric datasets including ADNI, PPMI, MIRIAD, UK Biobank

Demonstrates potential for distributed analysis of brain disorders

Abstract

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