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Integration of Federated Learning and Blockchain in Healthcare: A Tutorial
  • +2
  • Yahya Shahsavari,
  • Oussama A. Dambri,
  • Yaser Baseri,
  • Abdelhakim Senhaji Hafid,
  • Dimitrios Makrakis
Yahya Shahsavari

Corresponding Author:[email protected]

Author Profile
Oussama A. Dambri
Yaser Baseri
Abdelhakim Senhaji Hafid
Dimitrios Makrakis


Wearable devices and medical sensors revolutionize health monitoring, raising concerns about data privacy in Machine Learning (ML) for healthcare. This tutorial explores Federated Learning (FL) and Blockchain (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 Blockchain-based Federated Learning (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.
13 Apr 2024Submitted to TechRxiv
18 Apr 2024Published in TechRxiv