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Despite the general success of employing artificial intelligence (AI) to help radiologists perform computer-aided patient diagnosis, creating good models with tiny datasets at different sites is still tough. Medical image analysis is crucial for the quick and precise detection of lung disease and helps clinicians treat patients effectively while averting more fatalities. This is why real-time medical data management is becoming essential in the healthcare industry, especially for systems that monitor patients from a distance. To overcome this challenge, we propose a different approach by utilizing a relatively new learning framework. Individual sites may jointly train a global model using this approach, referred to as federated learning. Without explicitly sharing datasets, federated learning combines training results from various sites to produce a global model. This makes sure that patient confidentiality is upheld across all sites …