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The Role and Impact of Federal Learning in Digital Healthcare: A Useful Survey
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Author(s): Rajasree R. S. (New Horizon College of Engineering, India), Gopika G. S. (Sathyabama Institute of Science and Technology, India), Sree Krishna M. (Sathyabama Institute of Science and Technology, India)and Carlos Andrés Tavera Romero (Universidad Santiago de Cali, Colombia)
Copyright: 2022
Pages: 21
Source title:
Handbook of Research on Technical, Privacy, and Security Challenges in a Modern World
Source Author(s)/Editor(s): Amit Kumar Tyagi (National Institute of Fashion Technology, New Delhi, India)
DOI: 10.4018/978-1-6684-5250-9.ch007
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Abstract
During the COVID-19 pandemic, IoT and machine learning played a very important role in assisting doctors by remote patient monitoring. Machine learning and deep learning algorithms are used to process the data that are generated by IoT devices. However, there was major concern about the privacy of the patient data that is generated. The data that has been generated by the devices was sent to central servers which may cause data privacy issues. FL (federated learning), a type of machine learning, was created to address this problem. It provides a solution for data governance and privacy by processing the data rather than transferring the data to another location. The performance of FL models is better when compared to the models that are trained on datasets maintained centrally. In this work, certain insights are given on some of the challenges faced by the healthcare industry while employing digital healthcare techniques and how FL (federated learning) can improve the digital healthcare as well as how patient data can be preserved.
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