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Distributed Deep Learning for Smart IoMT Challenges in the Healthcare Domain

Distributed Deep Learning for Smart IoMT Challenges in the Healthcare Domain

Agila Harshini Thangavel
Copyright: © 2023 |Pages: 10
ISBN13: 9781668498040|ISBN10: 1668498049|ISBN13 Softcover: 9798369304457|EISBN13: 9781668498057
DOI: 10.4018/978-1-6684-9804-0.ch004
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MLA

Thangavel, Agila Harshini. "Distributed Deep Learning for Smart IoMT Challenges in the Healthcare Domain." Scalable and Distributed Machine Learning and Deep Learning Patterns, edited by J. Joshua Thomas, et al., IGI Global, 2023, pp. 65-74. https://doi.org/10.4018/978-1-6684-9804-0.ch004

APA

Thangavel, A. H. (2023). Distributed Deep Learning for Smart IoMT Challenges in the Healthcare Domain. In J. Thomas, S. Harini, & V. Pattabiraman (Eds.), Scalable and Distributed Machine Learning and Deep Learning Patterns (pp. 65-74). IGI Global. https://doi.org/10.4018/978-1-6684-9804-0.ch004

Chicago

Thangavel, Agila Harshini. "Distributed Deep Learning for Smart IoMT Challenges in the Healthcare Domain." In Scalable and Distributed Machine Learning and Deep Learning Patterns, edited by J. Joshua Thomas, S. Harini, and V. Pattabiraman, 65-74. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-9804-0.ch004

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Abstract

The Internet of Medical Things (IoMT) collects and transfers healthcare data over the network using sensors, software applications, and Edge devices. A greater number of Healthcare devices are being manufactured and there are various challenges like Interoperability, Security, Scalability, and privacy. IoMT devices are used to monitor and deliver treatments to patients remotely. For IoMt devices to reach their full potential the challenges need to be addressed. Healthcare devices when compromised can harm patients by disrupting personal data.

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