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Integration of Federated Learning and Blockchain in Healthcare: A Tutorial

Yahya Shahsavari, Oussama A. Dambri, Yaser Baseri, Abdelhakim Senhaji Hafid, Dimitrios Makrakis

TL;DR

This paper addresses privacy challenges in machine learning for healthcare by integrating Federated Learning (FL) with Blockchain (BC), enabling decentralized, privacy-preserving collaboration across institutions. It provides a data-type taxonomy for medical data and outlines three integration architectures—fully coupled, semi-coupled, and loosely coupled—along with BCFL applications in disease prediction, medical imaging, patient monitoring, and drug discovery. A detailed survey of related works demonstrates the current landscape, gaps, and the potential of blockchain-enabled federated learning to enhance data integrity, traceability, and incentives. The work highlights practical implications for secure and scalable healthcare analytics and identifies future directions, including regulatory considerations, edge computing, and cross-chain interoperability. Overall, BCFL promises to improve patient outcomes by enabling robust, privacy-preserving collaboration across distributed healthcare data sources.

Abstract

Wearable devices and medical sensors revolutionize health monitoring, raising concerns about data privacy in ML for healthcare. This tutorial explores FL and BC integration, offering a secure and privacy-preserving approach to healthcare analytics. FL enables decentralized model training on local devices at healthcare institutions, keeping patient data localized. This facilitates collaborative model development without compromising privacy. However, FL introduces vulnerabilities. BC, with its tamper-proof ledger and smart contracts, provides a robust framework for secure collaborative learning in FL. After presenting a taxonomy for the various types of data used in ML in medical applications, and a concise review of ML techniques for healthcare use cases, this tutorial explores three integration architectures for balancing decentralization, scalability, and reliability in healthcare data. Furthermore, it investigates how BCFL enhances data security and collaboration in disease prediction, medical image analysis, patient monitoring, and drug discovery. By providing a tutorial on FL, blockchain, and their integration, along with a review of BCFL applications, this paper serves as a valuable resource for researchers and practitioners seeking to leverage these technologies for secure and privacy-preserving healthcare ML. It aims to accelerate advancements in secure and collaborative healthcare analytics, ultimately improving patient outcomes.

Integration of Federated Learning and Blockchain in Healthcare: A Tutorial

TL;DR

This paper addresses privacy challenges in machine learning for healthcare by integrating Federated Learning (FL) with Blockchain (BC), enabling decentralized, privacy-preserving collaboration across institutions. It provides a data-type taxonomy for medical data and outlines three integration architectures—fully coupled, semi-coupled, and loosely coupled—along with BCFL applications in disease prediction, medical imaging, patient monitoring, and drug discovery. A detailed survey of related works demonstrates the current landscape, gaps, and the potential of blockchain-enabled federated learning to enhance data integrity, traceability, and incentives. The work highlights practical implications for secure and scalable healthcare analytics and identifies future directions, including regulatory considerations, edge computing, and cross-chain interoperability. Overall, BCFL promises to improve patient outcomes by enabling robust, privacy-preserving collaboration across distributed healthcare data sources.

Abstract

Wearable devices and medical sensors revolutionize health monitoring, raising concerns about data privacy in ML for healthcare. This tutorial explores FL and BC integration, offering a secure and privacy-preserving approach to healthcare analytics. FL enables decentralized model training on local devices at healthcare institutions, keeping patient data localized. This facilitates collaborative model development without compromising privacy. However, FL introduces vulnerabilities. BC, with its tamper-proof ledger and smart contracts, provides a robust framework for secure collaborative learning in FL. After presenting a taxonomy for the various types of data used in ML in medical applications, and a concise review of ML techniques for healthcare use cases, this tutorial explores three integration architectures for balancing decentralization, scalability, and reliability in healthcare data. Furthermore, it investigates how BCFL enhances data security and collaboration in disease prediction, medical image analysis, patient monitoring, and drug discovery. By providing a tutorial on FL, blockchain, and their integration, along with a review of BCFL applications, this paper serves as a valuable resource for researchers and practitioners seeking to leverage these technologies for secure and privacy-preserving healthcare ML. It aims to accelerate advancements in secure and collaborative healthcare analytics, ultimately improving patient outcomes.
Paper Structure (31 sections, 4 figures, 2 tables)

This paper contains 31 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Medical Data and Applications in Machine Learning
  3. Electronic Health Records
  4. Medical Imaging Data
  5. Genomic Data
  6. Biometric Data
  7. Sensor Data
  8. Patient-Generated Data
  9. Clinical Trial Data
  10. Prescription and Medication Data
  11. Laboratory Data
  12. Telehealth Data
  13. Artificial Intelligence
  14. Evolution of in Healthcare
  15. Potential of in Healthcare
  16. ...and 16 more sections

Figures (4)

  • Figure 1: Taxonomy of Medical Data Used for Machine Learning in Medical Applications
  • Figure 2: Exploring the Convergence of Artificial Intelligence with Healthcare: Trends, Applications, and Future Perspectives.
  • Figure 5: Blockchain Layers
  • Figure 6: Fully Coupled Architecture