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

AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT