Federated Learning on Heterogenous Data using Chest CT
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Large data have accelerated advances in AI. While it is well known that population differences from genetics, sex, race, diet, and various environmental factors contribute significantly 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 in medicine 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 present three techniques: Fed Averaging (FedAvg), Incremental Institutional Learning (IIL), and Cyclical Incremental Institutional Learning (CIIL). We also propose an FL strategy that leverages synthetically generated data to overcome class imbalances and data size disparities across centers. We show that FL can achieve comparable performance to Centralized Data Sharing (CDS) while maintaining high performance across sites with small, underrepresented data. We investigate the strengths and weaknesses for all technical approaches on this heterogeneous dataset including the robustness to non-Independent and identically distributed (non-IID) diversity of data. We also describe the sources of data heterogeneity such as age, sex, and site locations in the context of FL and show how even among the correctly labeled populations, disparities can arise due to these biases.
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