Abstract
In the modern era, there is a boom in automating medical diagnosis by
adopting emerging technologies and advanced applications of artificial intelligence.
These technologies require a huge amount of data for training the models and precisely
predicting the disease or disorder. Multiple organizations can contribute data for such
systems but maintaining data privacy while sharing the data is a major challenge. Also,
provisioning a large data corpus for the performance improvement of machine learning
and deep learning models in the healthcare domain while keeping the patient’s medical
confidentiality intact is a point of concern. Thus, there is a strong need to preserve the
privacy of medical data. This calls for the use of up-to-the-minute technologies where
the necessity of sharing raw data is completely eradicated, while each organization
receives a catered infrastructure for processing data. A cross-silo federated learning
model is based on the concept of decentralized data weights collection from multiple
clients which are then processed on the central server for modeling and aggregation,
thus maintaining data privacy in its true sense. The authors in this manuscript provide a
detailed comparative study of the different deep learning-based models in federated
learning and how efficiently they can classify lung X-Ray images into three classes:
Covid-19, Pneumonia, and Normal. This study can provide a benchmark for the
researchers looking forward to deep learning-based model applications of cross-silo
federated learning in healthcare.
Keywords: Covid-19, Diagnosis, Deep learning, Federated, Medical, Machine learning, Segmentation, X-Ray.