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A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN
This paper proposes a secure and distributed system for lung disease diagnosis, specifically COVID-19 and Pneumonia, using a hybrid federated learning-enabled ensemble model. It combines established CNN architectures with the SWIN Transformer to enhance diagnostic accuracy and ensure patient data privacy through federated learning.