Federated Learning Approach for Predicting the Growth Rate and Menace of COVID-19
Keywords:
Federated Learning, EHR, COVID-19, SVM, Logistic Regression, Single-Layer PerceptronAbstract
When attempting to use digital clinical data to predict the spread and threat of COVID-19, data available at a particular site is not sufficient for detecting COVID-19 detection. It also includes certain issues that include integrating data from multiple sources, and the concerns relevant to privacy while handling centralized database that comprises of sensitive data. Provides a framework which involves federated learning approach, that may use locally stored clinical data from several sites to develop a centralized COVID-19 prediction model. Suggest two unique approaches to local model aggregation to enhance the global model's predictive performance. This suggested method achieves performance on par with centralized learning and is better than localized learning models through extensive experimental assessment utilizing real-world health data from government sites. Additionally, aggregate approaches beat novel techniques in terms of Recall, Accuracy and Precision for a wide range of data distributions.
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