Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care

Fadila Zerka*, Samir Barakat, Sean Walsh, Marta Bogowicz, Ralph T. H. Leijenaar, Arthur Jochems, Benjamin Miraglio, David Townend, Philippe Lambin

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review


Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of non-communicable diseases. However, data centralization for big data raises privacy and regulatory concerns.

Covered topics include ( 1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; ( 3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives.

Distributed learning from federated databases makes data centralization unnecessary. Distributed algorithms iteratively analyze separate databases, essentially sharing research questions and answers between databases instead of sharing the data. In other words, one can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes.

Distributed learning promises great potential to facilitate big data for medical application, in particular for international consortiums. Our purpose is to review the major implementations of distributed learning in health care. (c) 2020 by American Society of Clinical Oncology

Original languageEnglish
Pages (from-to)184-200
Number of pages17
JournalJCO Clinical Cancer Informatics
Publication statusPublished - 5 Mar 2020



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