Federated Learning in Risk Prediction: A Primer and Application to COVID-19-Associated Acute Kidney Injury
Abstract
BACKGROUND: Modern machine learning and deep learning algorithms require large amounts of data; however, data sharing between multiple healthcare institutions is limited by privacy and security concerns.
SUMMARY: Federated learning provides a functional alternative to the single-institution approach while avoiding the pitfalls of data sharing. In cross-silo federated learning, the data do not leave a site. The raw data are stored at the site of collection. Models are created at the site of collection and are updated locally to achieve a learning objective. We demonstrate a use case with COVID-19-associated AKI. We showed that federated models outperformed their local counterparts, even when evaluated on local data in the test dataset, and performance was like those being used for pooled data. Increases in performance at a given hospital were inversely proportional to dataset size at a given hospital, which suggests that hospitals with smaller datasets have significant room for growth with federated learning approaches.
KEY MESSAGES: This short article provides an overview of federated learning, gives a use case for COVID-19-associated acute kidney injury, and finally details the issues along with some potential solutions.
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