Domain adaptive federated learning for multi-institution molecular mutation prediction and bias identification
Walia Farzana,
Megan A. Witherow,
Isaac Longoria
et al.
Abstract:Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and … Show more
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