A Systematic Survey of Blockchained Federated Learning

TL;DR

This paper provides a comprehensive survey of how blockchain technology enhances federated learning by addressing privacy, security, and scalability issues, and discusses current applications, challenges, and future directions.

Contribution

It systematically reviews the integration of blockchain with federated learning, highlighting mechanisms, applications, and open research challenges.

Findings

Blockchain improves security and privacy in federated learning.

BCFL addresses single-point failure and malicious data issues.

The survey identifies key challenges and future research directions.

Abstract

With the technological advances in machine learning, effective ways are available to process the huge amount of data generated in real life. However, issues of privacy and scalability will constrain the development of machine learning. Federated learning (FL) can prevent privacy leakage by assigning training tasks to multiple clients, thus separating the central server from the local devices. However, FL still suffers from shortcomings such as single-point-failure and malicious data. The emergence of blockchain provides a secure and efficient solution for the deployment of FL. In this paper, we conduct a comprehensive survey of the literature on blockchained FL (BCFL). First, we investigate how blockchain can be applied to federal learning from the perspective of system composition. Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL. We also survey the applications of BCFL in reality. Finally, we discuss some challenges and future research directions.