Abstract
Privacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned models that are trained in a distributed fashion. These techniques facilitate the calculation of research study endpoints such that private data never leaves a given device or healthcare system. We show on a diverse set of health studies that federated models can achieve the same level of accuracy, precision, and generalizability, and result in the same interpretation as standard centralized statistical models whilst achieving significantly stronger privacy protections. This work is the first to apply modern and general federated learning methods to clinical and epidemiological research -- across a spectrum of units of federation and model architectures. As a result, it enables health research participants to remain in control of their data and still contribute to advancing science -- aspects that used to be at odds with each other.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This work was partially supported by National Institutes of Health (NIH) Grant R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF DIBBS Grant OAC-1443054, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541. BR and JSB acknowledge funding from Google.org. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
It is the Advarra IRB's assessment that the study, which has already been conducted, did not meet the definition of human subjects research at 45 CFR 46.102, and IRB oversight was not required.
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
Yes
Data Availability
All data sources used in this manuscript are publicly available and were used in accordance with their applicable licensing. Links for each dataset are available at the links below.
https://figshare.com/articles/dataset/Malignancy_in_SARS-CoV2_infection/12666698
https://edoc.rki.de/handle/176904/7480
https://www.kaggle.com/uciml/pima-indians-diabetes-database
https://physionet.org/content/mimiciii/1.4/
https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/56NCVU
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/MQYM5S
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TA1OII