Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals.

ABSTRACT:

Objective

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 model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations.

Materials and methods

We leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompas

SUBMITTER: Peng L 

PROVIDER: S-EPMC9619688 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature