Explainable Federated and Blockchain-Enabled Cybersecurity for Smart Healthcare in Smart Cities: A Comprehensive Survey
DOI:
https://doi.org/10.66849/JIADS.v1i1.7Keywords:
Explainable Artificial Intelligence, Federated Learning, Blockchain, Healthcare 5.0Abstract
The increasing reliance on Cyber-Physical Systems (CPS) and Internet of Medical Things (IoMT) technologies in smart healthcare environments has raised critical concerns regarding data privacy, system security, and the transparency of automated decision-making. Conventional centralized security models are no longer sufficient to meet these demands in distributed and resource-constrained healthcare infrastructures. This paper presents a comprehensive survey of recent cybersecurity frameworks that integrate Explainable Artificial Intelligence (XAI), Federated Learning (FL), and Blockchain technologies to enhance trust and resilience in smart healthcare systems. Research published between 2023 and 2025 is systematically reviewed and classified using a novel taxonomy that groups existing studies into XAI-based, FL-based, hybrid, and fully integrated approaches. The surveyed frameworks are critically analyzed across key security dimensions, including privacy preservation, interpretability, resistance to tampering, robustness against adversarial attacks, scalability, and suitability for real-time deployment. The analysis indicates that while hybrid approaches partially address specific security challenges, they often involve trade-offs between transparency, privacy, and integrity. Moreover, only a limited number of studies attempt a unified integration of all three technologies. These findings highlight a clear research gap and underline the need for lightweight, scalable, and privacy-preserving security frameworks to support trustworthy Healthcare 5.0 systems.
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