VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange.
Journal
AMIA ... Annual Symposium proceedings. AMIA Symposium
ISSN: 1942-597X
Titre abrégé: AMIA Annu Symp Proc
Pays: United States
ID NLM: 101209213
Informations de publication
Date de publication:
2020
2020
Historique:
entrez:
3
5
2021
pubmed:
4
5
2021
medline:
5
6
2021
Statut:
epublish
Résumé
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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
870-877Informations de copyright
©2020 AMIA - All rights reserved.
Références
J Am Med Inform Assoc. 2016 May;23(3):570-9
pubmed: 26554428
Eur J Cancer. 2018 Nov;103:356-387
pubmed: 30100160
Sci Data. 2016 Mar 15;3:160018
pubmed: 26978244
J Am Med Inform Assoc. 2015 Nov;22(6):1212-9
pubmed: 26159465
Int J Epidemiol. 2014 Dec;43(6):1929-44
pubmed: 25261970
Eur J Cancer. 2018 Nov;104:70-80
pubmed: 30336359