  While it is well known that population differences from genetics, sex, race,
and environmental factors contribute to disease, AI studies in medicine have
largely focused on locoregional patient cohorts with less diverse data sources.
Such limitation stems from barriers to large-scale data share and ethical
concerns over data privacy. Federated learning (FL) is one potential pathway
for AI development that enables learning across hospitals without data share.
In this study, we show the results of various FL strategies on one of the
largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals
across five continents that comprise >10,000 patients with >1 million images.
We also propose an FL strategy that leverages synthetically generated data to
overcome class and size imbalances. We also describe the sources of data
heterogeneity in the context of FL, and show how even among the correctly
labeled populations, disparities can arise due to these biases.
