Federated Learning for Diabetic Retinopathy: Enhancing Detection with Data Privacy
Abstract
Federated learning (FL) has the potential to revolutionize the healthcare industry by facilitating collaborative machine learning without sacrificing data ownership rights across various institutions. Due to the fact that none of them are permitted while working with electronic health records (EHRs), medical imaging, or genetic data, the most significant use cases in the healthcare industry are those pertaining to patient data privacy and regulatory compliance. FL solves this by allowing organizations to jointly train machine learning models, all the while maintaining the security and decentralization of the data. The FL framework in healthcare is examined in this chapter, which has the potential to significantly improve patient privacy, predictive analytics, and individualized treatment while increasing diagnostic accuracy. It is important to note that this discussion focuses on FL and the reasons that, as explained at the beginning of the chapter, FL is superior to any traditional machine learning scheme when used for healthcare purposes. It continues by examining the unique challenges that the healthcare industry poses for machine learning, such as data privacy, heterogeneity, and label scarcity. Then, for artificial intelligence (AI)-driven practical demands pertaining to other benefits of FL like model generalization and privacy preservation, we provide an extensive use-case using FL applied to diabetic retinopathy detection. It also contains a thorough literature…
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