Integration of Federated Learning and Blockchain in Healthcare: A Tutorial

TL;DR

This tutorial reviews how integrating federated learning and blockchain can enhance data privacy, security, and collaboration in healthcare analytics, enabling secure, decentralized medical data processing and model sharing.

Contribution

It provides a comprehensive taxonomy, architecture options, and application insights for combining federated learning with blockchain in healthcare.

Findings

BCFL improves data security in healthcare applications

Three architectures balance decentralization, scalability, and reliability

Enhanced collaboration in disease prediction and medical imaging

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.