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The digital transformation of health sector is grappling with severe privacy and data governance challenges due to stringent laws and the sensitivity of medical data. Federated Learning (FL) is proposed as a privacy-preserving methodology for the collaboration of AI model training across decentralized sources of data without sharing patient data in the raw form. In this paper, we analyze the potential of FL in the context of digital healthcare covering areas such as medical imaging, Electronic Health Records, wearables, genomics, and drug discovery, to break the silos, enforce compliance and to increase model generalizability. FL paradigms alleviate privacy/security concerns of a central pool of combined data, and have applications in tumor segmentation, readmission prediction, real-time health monitoring. However, many technical obstacles remain, such as data heterogeneity, communication overhead, and security issues, as well as the requirement for personalized models. This chapter also presents solutions based on FedProx, differential privacy, and secure multi-party computation. Work on FL in the industry and academia show the practical feasibility of FL. Looking forward, blockchain based solutions for audibility, and adapting to low-resource environments, Explainable AI, and standards are directions for further exploration. FL offers a revolutionary way of reconciling innovation with privacy in healthcare AI.

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The Convergence of Federated Learning for the Digital Healthcare Market: An Overview

  • 章节

  • 发表日期：

    2026年

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  • Kanchan G. Rajput,
  • Anil Sharma,
  • Benila Susan Jacob,
  • Pandya Abhishek Devendrabhai

摘要

The digital transformation of health sector is grappling with severe privacy and data governance challenges due to stringent laws and the sensitivity of medical data. Federated Learning (FL) is proposed as a privacy-preserving methodology for the collaboration of AI model training across decentralized sources of data without sharing patient data in the raw form. In this paper, we analyze the potential of FL in the context of digital healthcare covering areas such as medical imaging, Electronic Health Records, wearables, genomics, and drug discovery, to break the silos, enforce compliance and to increase model generalizability. FL paradigms alleviate privacy/security concerns of a central pool of combined data, and have applications in tumor segmentation, readmission prediction, real-time health monitoring. However, many technical obstacles remain, such as data heterogeneity, communication overhead, and security issues, as well as the requirement for personalized models. This chapter also presents solutions based on FedProx, differential privacy, and secure multi-party computation. Work on FL in the industry and academia show the practical feasibility of FL. Looking forward, blockchain based solutions for audibility, and adapting to low-resource environments, Explainable AI, and standards are directions for further exploration. FL offers a revolutionary way of reconciling innovation with privacy in healthcare AI.