Trends in Blockchain and Federated Learning for Data Sharing in Distributed Platforms

TL;DR

This paper reviews recent trends in combining blockchain and federated learning to enhance secure, privacy-preserving data sharing in distributed IoT and wireless networks, highlighting applications in industry, vehicles, and healthcare.

Contribution

It provides a comprehensive survey of blockchain and federated learning integration, emphasizing their roles in secure data sharing across various IoT applications.

Findings

Blockchain enhances security in federated learning systems.

Federated learning reduces data privacy risks.

Integration enables secure, decentralized data sharing.

Abstract

With the development of communication technologies in 5G networks and the Internet of things (IoT), a massive amount of generated data can improve machine learning (ML) inference through data sharing. However, security and privacy concerns are major obstacles in distributed and wireless networks. In addition, IoT has a limitation on system resources depending on the purpose of services. In addition, a blockchain technology enables secure transactions among participants through consensus algorithms and encryption without a centralized coordinator. In this paper, we first review the federated leaning (FL) and blockchain mechanisms, and then, present a survey on the integration of blockchain and FL for data sharing in industrial, vehicle, and healthcare applications.