VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange.
| Autor: | Moncada-Torres A; Netherlands Comprehensive Cancer Organization (IKNL), Eindhoven, NL., Martin F; Netherlands Comprehensive Cancer Organization (IKNL), Eindhoven, NL., Sieswerda M; Netherlands Comprehensive Cancer Organization (IKNL), Eindhoven, NL., Van Soest J; Maastricht University Medical Centre+, Maastricht, NL., Geleijnse G; Netherlands Comprehensive Cancer Organization (IKNL), Eindhoven, NL. |
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| Jazyk: | angličtina |
| Zdroj: | AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2021 Jan 25; Vol. 2020, pp. 870-877. Date of Electronic Publication: 2021 Jan 25 (Print Publication: 2020). |
| Abstrakt: | 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. (©2020 AMIA - All rights reserved.) |
| Databáze: | MEDLINE |
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