Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals.
Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described Coronavirus Disease 19 (COVID-19) diagnostic [...]
Author(s): Peng, Le, Luo, Gaoxiang, Walker, Andrew, Zaiman, Zachary, Jones, Emma K, Gupta, Hemant, Kersten, Kristopher, Burns, John L, Harle, Christopher A, Magoc, Tanja, Shickel, Benjamin, Steenburg, Scott D, Loftus, Tyler, Melton, Genevieve B, Gichoya, Judy Wawira, Sun, Ju, Tignanelli, Christopher J
DOI: 10.1093/jamia/ocac188