Abstract
In the medical or healthcare industry, where, the already available information or data is never sufficient, excellence can be performed with the help of Federated Learning (FL) by empowering AI models to learn on private data without conceding privacy. It opened the door for ample research because of its high level of communication efficiency which is linked with distributed training problems. The primary objective of the chapter is to highlight the adaptability and working of the FL techniques in the healthcare system especially in drug development, clinical diagnosis, digital health monitoring, and various disease predictions and detection system. The first section of the chapter is comprised of a background study on an FL framework for healthcare, FL working model in healthcare, and various important benefits of FL. The next section of the chapter described the reported work which highlights different research works in the field of electronic health record systems, drug discovery, and disease prediction systems using the FL model. The final section of the chapter presented the comparative analysis, which shows the comparison between different FL algorithms for different health sectors by using parameters such as accuracy, the area under the curve, precision, recall, and F-score.
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Kumar, Y., Singla, R. (2021). Federated Learning Systems for Healthcare: Perspective and Recent Progress. In: Rehman, M.H.u., Gaber, M.M. (eds) Federated Learning Systems. Studies in Computational Intelligence, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-030-70604-3_6
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