Role of Federated Learning in Healthcare: A Review

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.