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

TL;DR

This survey provides a comprehensive review of recent research on BC-FL systems, analyzing the benefits and challenges associated with blockchain integration and offering insights on future research directions for the BC-FL system.

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.

Figures (11)

• Figure 1: Main scope of this survey. We begin by exploring the characteristics of blockchain and investigate its enhancement of Federated Learning systems. Next, we discuss the additional challenges introduced by using blockchain in FL systems and review existing solutions. Finally, we outline future research directions for Blockchain-empowered Federated Learning systems.
• Figure 2: Categories of FL systems.
• Figure 3: Standard federated learning (FL) training methodology.
• Figure 4: The structure of a blockchain.
• Figure 5: Two decentralized architectures of the Blockchain-Empowered Federated Learning system.