Blockchain Integrated Federated Learning in Edge-Fog-Cloud Systems for IoT based Healthcare Applications A Survey

TL;DR

This survey reviews how blockchain and federated learning can be integrated in edge-fog-cloud IoT systems to enhance privacy and security in healthcare applications, highlighting architectures, functions, and implementations.

Contribution

It provides a comprehensive overview of the integration of blockchain and federated learning in IoT healthcare, detailing architectures, applications, and evaluation of implementations.

Findings

Blockchain enhances data security and integrity in federated learning.

Federated learning preserves user privacy while enabling collaborative model training.

Integration of blockchain and federated learning improves trustworthiness in healthcare IoT systems.

Abstract

Modern Internet of Things (IoT) applications generate enormous amounts of data, making data-driven machine learning essential for developing precise and reliable statistical models. However, data is often stored in silos, and strict user-privacy legislation complicates data utilization, limiting machine learning's potential in traditional centralized paradigms due to diverse data probability distributions and lack of personalization. Federated learning, a new distributed paradigm, supports collaborative learning while preserving privacy, making it ideal for IoT applications. By employing cryptographic techniques, IoT systems can securely store and transmit data, ensuring consistency. The integration of federated learning and blockchain is particularly advantageous for handling sensitive data, such as in healthcare. Despite the potential of these technologies, a comprehensive examination of their integration in edge-fog-cloud-based IoT computing systems and healthcare applications is needed. This survey article explores the architecture, structure, functions, and characteristics of federated learning and blockchain, their applications in various computing paradigms, and evaluates their implementations in healthcare.