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

Rabia Abid1, Muhammad Rizwan2, Abdulatif Alabdulatif3,*, Abdullah Alnajim4, Meznah Alamro5, Mourade Azrour6

1 Department of Computer Science, Kinnaird College for Women, Lahore, Pakistan
2 College of Science and Engineering, University of Derby, Derby, DE221GB, UK
3 Department of Computer Science, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia
4 Department of Information Technology, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia
5 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 84428, Saudi Arabia
6 STI Laboratory, IDMS Team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Meknes, Morocco

* Corresponding Author: Abdulatif Alabdulatif. Email: email

Computers, Materials & Continua 2024, 78(3), 3413-3429. https://doi.org/10.32604/cmc.2024.046880

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.

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APA Style
Abid, R., Rizwan, M., Alabdulatif, A., Alnajim, A., Alamro, M. et al. (2024). Adaptation of federated explainable artificial intelligence for efficient and secure e-healthcare systems. Computers, Materials & Continua, 78(3), 3413-3429. https://doi.org/10.32604/cmc.2024.046880
Vancouver Style
Abid R, Rizwan M, Alabdulatif A, Alnajim A, Alamro M, Azrour M. Adaptation of federated explainable artificial intelligence for efficient and secure e-healthcare systems. Comput Mater Contin. 2024;78(3):3413-3429 https://doi.org/10.32604/cmc.2024.046880
IEEE Style
R. Abid, M. Rizwan, A. Alabdulatif, A. Alnajim, M. Alamro, and M. Azrour "Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems," Comput. Mater. Contin., vol. 78, no. 3, pp. 3413-3429. 2024. https://doi.org/10.32604/cmc.2024.046880



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