Decentralized Healthcare Systems with Federated Learning and Blockchain

TL;DR

This paper reviews how blockchain and federated learning can be combined to enable secure, decentralized sharing of healthcare data, addressing privacy concerns while facilitating advanced AI model training.

Contribution

It provides a comprehensive overview of current methods integrating blockchain and federated learning in decentralized healthcare systems.

Findings

Blockchain enhances data security and integrity.

Federated learning enables collaborative model training without data sharing.

Current approaches face scalability and interoperability challenges.

Abstract

Artificial intelligence (AI) and deep learning techniques have gained significant attraction in recent years, owing to their remarkable capability of achieving high performance across a broad range of applications. However, a crucial challenge in training such models is the acquisition of vast amounts of data, which is often limited in fields like healthcare. In this domain, medical data is typically scattered across various sources such as hospitals, clinics, and wearable devices. The aggregated data collected from multiple sources in the healthcare domain is sufficient for training advanced deep learning models. However, these sources are frequently hesitant to share such data due to privacy considerations. To address this challenge, researchers have proposed the integration of blockchain and federated learning to develop a system that facilitates the secure sharing of medical records. This work provides a succinct review of the current state of the art in the use of blockchain and federated learning in the decentralized healthcare domain.