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

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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