A Comparative Study of Federated Learning Methods for COVID-19 Detection

Abstract Deep learning has proven to be highly effective in diagnosing COVID-19; however, its efficacy is contingent upon the availability of extensive data for model training. The data sharing among hospitals, which is crucial for training robust models, is often restricted by privacy regulations. Federated learning (FL) emerges as a solution by enabling model training across multiple hospitals while preserving data privacy. However, the deployment of FL can be resource-intensive, necessitating efficient utilization of computational and network resources. In this study, we evaluate the performance and resource efficiency of five FL algorithms in the context of COVID-19 detection using Convolutional Neural Networks (CNNs) in a decentralized setting. The evaluation involves varying the number of participating entities, the number of federated rounds, and the selection algorithms. Our findings indicate that the Cyclic Weight Transfer algorithm exhibits superior performance, particularly when the number of participating hospitals is limited. These insights hold practical implications for the deployment of FL algorithms in COVID-19 detection and broader medical image analysis.