Fold-stratified cross-validation for unbiased and privacy-preserving federated learning.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Aug 1, 2020

Abstract

OBJECTIVE: We introduce fold-stratified cross-validation, a validation methodology that is compatible with privacy-preserving federated learning and that prevents data leakage caused by duplicates of electronic health records (EHRs).

Authors

  • Romain Bey
    Centre of Research in Epidemiology and Statistics (CRESS), Université de Paris, French Institute of Health and Medical Research (INSERM), National Institute of Agricultural Research (INRA), Paris, France.
  • Romain Goussault
    CIC 1413, Center for Research in Cancerology and Immunology Nantes-Angers (CRCINA), Dermatology Department, Centre Hospitalier Universitaire Nantes, Nantes University, Nantes, France.
  • François Grolleau
    Centre of Research in Epidemiology and Statistics (CRESS), Université de Paris, French Institute of Health and Medical Research (INSERM), National Institute of Agricultural Research (INRA), Paris, France.
  • Mehdi Benchoufi
    Centre of Research in Epidemiology and Statistics (CRESS), Université de Paris, French Institute of Health and Medical Research (INSERM), National Institute of Agricultural Research (INRA), Paris, France.
  • Raphaël Porcher
    Université de Paris, Epidemiology and Statistics Research Center (CRESS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Recherche Agronomique (INRA), F-75004, Paris, France.

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