Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

Journal article


Abid, R., Rizwan, M., Alabdulatif, A., Alnajim, A., Alamro, M. and Azrour, M. 2024. Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems. CMC-Computers, Materials & Continua. pp. 1-17. https://doi.org/10.32604/cmc.2024.046880
AuthorsAbid, R., Rizwan, M., Alabdulatif, A., Alnajim, A., Alamro, M. and Azrour, M.
Abstract

Explainable Artificial Intelligence (XAI) has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning (ML) and Deep Learning (DL) based algorithms. In this paper, we chose e-healthcare systems for efficient decision-making and data classification, especially in data security, data handling, diagnostics, laboratories, and decision-making. Federated Machine Learning (FML) is a new and advanced technology that helps to maintain privacy for Personal Health Records (PHR) and handle a large amount of medical data effectively. In this context, XAI, along with FML, increases efficiency and improves the security of e-healthcare systems. The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning (FL) platform. The experimental evaluation demonstrates the accuracy rate by taking epochs size 5, batch size 16, and the number of clients 5, which shows a higher accuracy rate (19, 104). We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

KeywordsArtificial Intelligence; Data privacy; Federated Machine Learning; ; Healthcare system; Security
Year2024
JournalCMC-Computers, Materials & Continua
Journal citationpp. 1-17
PublisherTech Science Press
ISSN1546-2218
Digital Object Identifier (DOI)https://doi.org/10.32604/cmc.2024.046880
Web address (URL)https://www.techscience.com/cmc/online/detail/20158
Accepted author manuscript
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Open
Output statusPublished
Publication dates
Online19 Mar 2024
Publication process dates
Accepted05 Jan 2024
Deposited07 Mar 2024
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https://repository.derby.ac.uk/item/q4vz2/adaptation-of-federated-explainable-artificial-intelligence-for-efficient-and-secure-e-healthcare-systems

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