A Simulated Federated Analysis of MS-Induced Brain Lesions

TL;DR

This paper presents a simulation framework for federated analysis of MS patient data, combining synthetic cohorts and real datasets to emulate real-world multi-center research while preserving privacy.

Contribution

It introduces a realistic simulation environment for federated MS research, integrating image segmentation, survival analysis, and PCA with synthetic and real data.

Findings

Framework accurately replicates federated workflows

Enables evaluation of federated learning methods in MS research

Supports benchmarking with high-fidelity synthetic cohorts

Abstract

Federated techniques such as federated learning and federated analysis have emerged as a powerful paradigm for enabling multi-center research on sensitive clinical data while preserving patient privacy. In this study, we introduce a simulation framework that emulates a real-world federated research project focused on the analysis of multiple sclerosis (MS) patient data. The project comprises two components: an image segmentation task and a clinical data analysis task, where federated variants of survival analysis and Principal Component Analysis (PCA) are employed. To capture the complexity and heterogeneity of real clinical datasets, we construct a federation of high-fidelity synthetic cohorts designed to mirror MS-related clinical and demographic characteristics, while the imaging component leverages publicly available real-world datasets. Our simulation replicates key elements of authentic federated workflows, including distributed data governance, site-specific preprocessing, model training across isolated nodes, and the secure aggregation of analytical outputs. This framework provides a realistic testbed for developing, evaluating, and benchmarking federated learning methods in the context of MS research.