A Simulated Federated Analysis of MS-Induced Brain Lesions

• URL: http://arxiv.org/abs/2605.08223v1
• Date: Wed, 06 May 2026 14:55:51 GMT
• Title: A Simulated Federated Analysis of MS-Induced Brain Lesions
• Abstract summary: We introduce a simulation framework that emulates a real-world federated research project focused on the analysis of multiple sclerosis (MS) patient data.<n>We construct a federation of high-fidelity synthetic cohorts designed to mirror MS-related clinical and demographic characteristics.<n>This framework provides a realistic testbed for developing, evaluating, and benchmarking federated learning methods in the context of MS research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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