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.
| Comments: | Accepted for publication at The 39th IEEE International Symposium on Computer-Based Medical Systems |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.08223 [cs.LG] |
| (or arXiv:2605.08223v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08223 arXiv-issued DOI via DataCite |
From: Evelyn Trautmann [view email]
[v1]
Wed, 6 May 2026 14:55:51 UTC (1,857 KB)
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