Federated learning for preserving data privacy in collaborative healthcare research

Tyler J. Loftus, Matthew M. Ruppert, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Jeremy A. Balch, Philip A. Efron, Gilbert R. Upchurch, Parisa Rashidi, Christopher Tignanelli, Jiang Bian, Azra Bihorac

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Generalizability, external validity, and reproducibility are high priorities for artificial intelligence applications in healthcare. Traditional approaches to addressing these elements involve sharing patient data between institutions or practice settings, which can compromise data privacy (individuals’ right to prevent the sharing and disclosure of information about themselves) and data security (simultaneously preserving confidentiality, accuracy, fidelity, and availability of data). This article describes insights from real-world implementation of federated learning techniques that offer opportunities to maintain both data privacy and availability via collaborative machine learning that shares knowledge, not data. Local models are trained separately on local data. As they train, they send local model updates (e.g. coefficients or gradients) for consolidation into a global model. In some use cases, global models outperform local models on new, previously unseen local datasets, suggesting that collaborative learning from a greater number of examples, including a greater number of rare cases, may improve predictive performance. Even when sharing model updates rather than data, privacy leakage can occur when adversaries perform property or membership inference attacks which can be used to ascertain information about the training set. Emerging techniques mitigate risk from adversarial attacks, allowing investigators to maintain both data privacy and availability in collaborative healthcare research. When data heterogeneity between participating centers is high, personalized algorithms may offer greater generalizability by improving performance on data from centers with proportionately smaller training sample sizes. Properly applied, federated learning has the potential to optimize the reproducibility and performance of collaborative learning while preserving data security and privacy.

Original languageEnglish (US)
JournalDigital Health
Volume8
DOIs
StatePublished - 2022

Bibliographical note

Funding Information:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: TJL was supported by the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health under Award Number K23GM140268 and by the Thomas H. Maren Fund. T.O.B. was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health grant K01DK120784, R01GM110240 from the National Institute of General Medical Sciences, and by UF Research AWD09459 and the Gatorade Trust, University of Florida. PR was supported by National Science Foundation CAREER award 1750192, P30AG028740 and R01AG05533 from the NIA, 1R21EB027344 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and R01GM110240 from the NIGMS. AB was supported by R01GM110240 from the NIGMS and 1R21EB027344 from the NIBIB. This work was supported in part by the National Center for Advancing Translational Sciences and Clinical and Translational Sciences Award to the University of Florida UL1TR000064. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

Publisher Copyright:
© The Author(s) 2022.

Keywords

  • Federated learning
  • data
  • deep learning
  • privacy
  • security

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