A Survey on Blockchain-Based Federated Learning and Data Privacy

TL;DR

This survey reviews blockchain-based federated learning, highlighting its privacy-preserving mechanisms, applications, and future research directions in secure decentralized machine learning.

Contribution

It provides a comprehensive comparison of privacy mechanisms in blockchain-based federated learning and identifies key research questions for future exploration.

Findings

Blockchain enhances data security in federated learning.

Various privacy-preserving mechanisms have been evaluated.

Future research directions are proposed for blockchain-based federated learning.

Abstract

Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the models transmission. This method reduces the costs and privacy concerns associated with centralized machine learning methods while ensuring data privacy by distributing training data across heterogeneous devices. On the other hand, federated learning has the drawback of data leakage due to the lack of privacy-preserving mechanisms employed during storage, transfer, and sharing, thus posing significant risks to data owners and suppliers. Blockchain technology has emerged as a promising technology for offering secure data-sharing platforms in federated learning, especially in Industrial Internet of Things (IIoT) settings. This survey aims to compare the performance and security of various data privacy mechanisms adopted in blockchain-based federated learning architectures. We conduct a systematic review of existing literature on secure data-sharing platforms for federated learning provided by blockchain technology, providing an in-depth overview of blockchain-based federated learning, its essential components, and discussing its principles, and potential applications. The primary contribution of this survey paper is to identify critical research questions and propose potential directions for future research in blockchain-based federated learning.