Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Review Article

The Amalgamation of Federated Learning and Explainable Artificial Intelligence for the Internet of Medical Things: A Review

In Press, (this is not the final "Version of Record"). Available online 12 December, 2023
Author(s): Chemmalar Selvi G, Ramalingam M, Gokul Yenduri, Deepti Raj G, Dasari Bhulakshmi, Dasaradharami Reddy K, Y Supriya, Thippa Reddy G, Rajkumar Singh Rathore and Rutvij H Jhaveri*
Published on: 12 December, 2023

Article ID: e121223224367

DOI: 10.2174/0126662558266152231128060222

Price: $95

Abstract

The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, integrating the Internet of Things (IoT) with medical devices, sensors, and healthcare systems. From peripheral devices that monitor vital signs to remote patient monitoring systems and smart hospitals, IoMT provides a vast array of applications that empower healthcare professionals. However, the integration of IoMT presents numerous obstacles, such as data security, privacy concerns, interoperability, scalability, and ethical considerations. For the successful integration and deployment of IoMT, addressing these obstacles is essential. Federated Learning (FL) permits collaborative model training while maintaining data privacy in distributed environments like IoMT. By incorporating Explainable Artificial Intelligence (XAI) techniques, the resulting models become more interpretable and transparent, enabling healthcare professionals to comprehend the underlying decision-making processes. This integration not only improves the credibility of Artificial Intelligence models but also facilitates the detection of biases, errors, and peculiar patterns in the data. The combination of FL and XAI contributes to the development of more privacy-preserving, trustworthy, and explainable AI systems, which are essential for the development of dependable and ethically sound IoMT applications. Hence, the aim of this paper is to conduct a literature review on the amalgamation of FL and XAI for IoMT.

Keywords: Wearable sensor devices, Federated Learning, Explainable AI, Trustworthiness, Data Privacy concerns, security


Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy