Integration of Federated Learning and Blockchain in Healthcare: A Tutorial

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 de...

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Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Shahsavari, Yahya, Dambri, Oussama A, Baseri, Yaser, Hafid, Abdelhakim Senhaji, Makrakis, Dimitrios
Format: Article
Language:English
Subjects:
Blockchain
Collaboration
Cryptography
Federated learning
Health care
Image analysis
Medical electronics
Medical imaging
Privacy
Taxonomy
Telemedicine
Wearable technology
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Summary: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.
ISSN:2331-8422