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

Chemmalar   Selvi   Govardanan; Ramalingam      Murugan; Gokul      Yenduri; Deepti   Raj   Gurrammagari; Dasari      Bhulakshmi; Dasaradharami   Reddy   Kandati; Yarradoddi      Supriya; Thippa   Reddy   Gadekallu; Rajkumar   Singh   Rathore; Rutvij   H   Jhaveri
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: Machine learning, CNNs, classification, prediction model, healthcare, artificial intelligence, internet of medical.
Graphical Abstract

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Cite As
Recent Advances in Computer Science and Communications

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

Abstract

Graphical Abstract
