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Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Here, we propose the adapted algorithm for the federated survival SVM, describe its implementation as a Feature Cloud app, and explain how we evaluated its performance. We extended the regression objective of scikit-survival’s Fast Survival SVM without ranking to be applicable in federated environments [24].

Julian Späth, Zeno Sewald, Niklas Probul, Magali Berland, Mathieu Almeida, Nicolas Pons, Emmanuelle Le Chatelier, Pere Ginès, Cristina Solé, Adrià Juanola, Josch Pauling, Jan Baumbach

JMIR AI 2024;3:e47652

The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research

The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research

These containers, referred to here as the federated pipelines, were deployed on each registry’s infrastructure and were mounted with data that had already been standardized and aligned with the data dictionary, facilitating seamless execution. After computing these buckets, they were transferred to the central platform. Multiple versions of these Docker containers were used to distribute scripts across the federated model sharing registries.

Ashkan Pirmani, Edward De Brouwer, Lotte Geys, Tina Parciak, Yves Moreau, Liesbet M Peeters

JMIR Med Inform 2023;11:e48030