Federated Learning-Based Techniques for COVID-19 Detection—A Systematic Review

The COVID-19 pandemic has created a significant need for accurate and rapid diagnosis of the disease. Traditional methods of diagnosis, such as PCR-based tests, have several limitations, including high cost, long turnaround times, and the need for specialised equipment and personnel. A promising technique for COVID-19 detection is federated learning (FL), which enables the cooperative training of machine learning models using distributed data sources while ensuring data privacy. This survey report provides an overview of the current state-of-the-art for COVID-19 detection utilising FL. We review the key concepts and principles of FL, and then discuss the various approaches used for COVID-19 detection, including deep learning-based approaches, transfer learning, and ensemble learning. We also examine the challenges and limitations of FL for COVID-19 detection, including data heterogeneity, communication overhead, and privacy concerns. Finally, we highlight the potential future directions of research in this area, including the development of more robust and scalable FL algorithms and the combination of FL with other cutting-edge technologies like edge computing and blockchain.