VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange

Answering many of the research questions in the field of cancer informatics requires incorporating and centralizing data that are hosted by different parties. Federated Learning (FL) has emerged as a new approach in which a global model can be generated without disclosing private patient data by kee...

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Bibliographic Details
Published inAMIA ... Annual Symposium proceedings Vol. 2020; pp. 870 - 877
Main Authors Moncada-Torres, Arturo, Martin, Frank, Sieswerda, Melle, Van Soest, Johan, Geleijnse, Gijs
Format Journal Article
LanguageEnglish
Published United States American Medical Informatics Association 2020
Subjects
Confidentiality
Humans
Learning
Machine Learning
Privacy
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Summary:Answering many of the research questions in the field of cancer informatics requires incorporating and centralizing data that are hosted by different parties. Federated Learning (FL) has emerged as a new approach in which a global model can be generated without disclosing private patient data by keeping them at their original location. Flexible, user-friendly, and robust infrastructures are crucial for bringing FL solutions to the day-to-day work of the cancer epidemiologist. In this paper, we present an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange, VANTAGE6. We provide a detailed description of its conceptual design, modular architecture, and components. We also show a few examples where VANTAGE6 has been successfully used in research on observational cancer data. Developing and deploying technology to support federated analyses - such as VANTAGE6 - will pave the way for the adoption and mainstream practice of this new approach for analyzing decentralized data.
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ISSN:1942-597X
1559-4076