Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions

TL;DR

This paper reviews how integrating blockchain with federated learning enhances security and fairness but introduces resource challenges, analyzing recent research, benefits, challenges, and future directions.

Contribution

It provides a comprehensive survey of blockchain-empowered federated learning, detailing benefits, challenges, implementation methods, and future research directions.

Findings

Blockchain improves security and fairness in FL.

Integration introduces resource demands on network and storage.

Future research needs to address scalability and efficiency.

Abstract

Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face limitations such as single points of failure, lack of incentives, and inadequate security. To address these challenges, blockchain technology is integrated into FL systems to provide stronger security, fairness, and scalability. However, blockchain-empowered FL (BC-FL) systems introduce additional demands on network, computing, and storage resources. This survey provides a comprehensive review of recent research on BC-FL systems, analyzing the benefits and challenges associated with blockchain integration. We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration. Additionally, we offer insights on future research directions for the BC-FL system.