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The combination of the Healthcare 5.0 and Federated Learning (FL) opens the new era of intelligent health systems and collaborative AI with data privacy. This chapter dwells on the role of FL in decentralized healthcare focusing on issues related to data silos, regulatory frameworks, and ethical challenges. We look at the main principles and architectures as well as privacy preserving mechanisms in FL and their applications in medical imaging, drug discovery, and pandemic response. Other essential technical challenges such as: data heterogeneity, comminution overhead, andGDPR/HIPAA compliance are examined plus the emerging coping mechanisms like edge computing, block chain-based FL, and many more. It is concluded by discussing the future (including integration with IoT, as well as global health equity), suggesting that FL can be used as a foundation on which scalable, patient-centric care can be built. This work can help researchers, policymakers, and practitioners to use the potential of the FL in Healthcare 5.0 and beyond since it bridges the gap between theoretical concepts and practical applications.

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Federated Learning for Decentralized Healthcare: Privacy, Efficiency, and Scalability in Healthcare 5.0

  • 章节

  • 发表日期：

    2026年

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  • Subrata Paul,
  • Anirban Mitra,
  • Shivnath Ghosh,
  • Amitava Podder

摘要

The combination of the Healthcare 5.0 and Federated Learning (FL) opens the new era of intelligent health systems and collaborative AI with data privacy. This chapter dwells on the role of FL in decentralized healthcare focusing on issues related to data silos, regulatory frameworks, and ethical challenges. We look at the main principles and architectures as well as privacy preserving mechanisms in FL and their applications in medical imaging, drug discovery, and pandemic response. Other essential technical challenges such as: data heterogeneity, comminution overhead, andGDPR/HIPAA compliance are examined plus the emerging coping mechanisms like edge computing, block chain-based FL, and many more. It is concluded by discussing the future (including integration with IoT, as well as global health equity), suggesting that FL can be used as a foundation on which scalable, patient-centric care can be built. This work can help researchers, policymakers, and practitioners to use the potential of the FL in Healthcare 5.0 and beyond since it bridges the gap between theoretical concepts and practical applications.