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<p>The advent of Healthcare 5.0 heralds a groundbreaking revolution in digital healthcare, superseding the achievements of its predecessor, Healthcare 4.0. Integrating cutting-edge technologies such as the Internet of Medical Things (IoMT), smart wearables, and the extraordinary capabilities of Artificial Intelligence (AI), Healthcare 5.0 envisions a unified framework that grants seamless access to health records, fosters interconnectedness among individuals, resources, and institutions, and empowers intelligent responses to medical concerns. However, the realization of Healthcare 5.0 faces a significant challenge in the form of high-speed data transmission using smart devices. Conventional AI approaches relying on centralized data processing raise compelling concerns surrounding information privacy and scalability within the Healthcare 5.0 context. Amidst this backdrop, federated learning emerges as a beacon of hope, offering a decentralized AI paradigm that facilitates on-device machine learning without compromising end-user privacy through centralized data export. Safeguarding data integrity, federated learning holds the key to unlocking the full potential of Healthcare 5.0. In this pioneering study, we conduct an extensive survey, exploring the transformative implications of federated learning within the realm of Healthcare 5.0. By shedding light on recent advancements tailored to this paradigm, we delve into the fundamental concepts of resource-awareness, privacy preservation, incentivization, and personalization, all within the framework of federated learning. Moreover, we meticulously scrutinize key parameters including security, sparsification, quantization, robustness, scalability, and privacy, providing an authentic evaluation of the current progress in federated learning for Healthcare 5.0. This comprehensive survey serves as an indispensable cornerstone for the evolution of Healthcare 5.0, offering invaluable insights into its unique requirements and untapped potential. By harnessing the capabilities of federated learning in this context, we envisage a transformative era in digital healthcare, fostering a more interconnected, secure, and intelligent healthcare landscape for the betterment of society.</p>

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Federated learning for Healthcare 5.0: a comprehensive survey, taxonomy, challenges, and solutions

  • 期刊论文

  • 发表日期：

    2025年1月1日

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  • Muhammad Sadiq Amin,
  • Shabir Ahmad,
  • Woong-Kee Loh

摘要

The advent of Healthcare 5.0 heralds a groundbreaking revolution in digital healthcare, superseding the achievements of its predecessor, Healthcare 4.0. Integrating cutting-edge technologies such as the Internet of Medical Things (IoMT), smart wearables, and the extraordinary capabilities of Artificial Intelligence (AI), Healthcare 5.0 envisions a unified framework that grants seamless access to health records, fosters interconnectedness among individuals, resources, and institutions, and empowers intelligent responses to medical concerns. However, the realization of Healthcare 5.0 faces a significant challenge in the form of high-speed data transmission using smart devices. Conventional AI approaches relying on centralized data processing raise compelling concerns surrounding information privacy and scalability within the Healthcare 5.0 context. Amidst this backdrop, federated learning emerges as a beacon of hope, offering a decentralized AI paradigm that facilitates on-device machine learning without compromising end-user privacy through centralized data export. Safeguarding data integrity, federated learning holds the key to unlocking the full potential of Healthcare 5.0. In this pioneering study, we conduct an extensive survey, exploring the transformative implications of federated learning within the realm of Healthcare 5.0. By shedding light on recent advancements tailored to this paradigm, we delve into the fundamental concepts of resource-awareness, privacy preservation, incentivization, and personalization, all within the framework of federated learning. Moreover, we meticulously scrutinize key parameters including security, sparsification, quantization, robustness, scalability, and privacy, providing an authentic evaluation of the current progress in federated learning for Healthcare 5.0. This comprehensive survey serves as an indispensable cornerstone for the evolution of Healthcare 5.0, offering invaluable insights into its unique requirements and untapped potential. By harnessing the capabilities of federated learning in this context, we envisage a transformative era in digital healthcare, fostering a more interconnected, secure, and intelligent healthcare landscape for the betterment of society.