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.

翻译：联邦学习（FL）是一种分布式机器学习方法，通过在客户端本地训练模型并在参数服务器上聚合模型来保护用户数据隐私。尽管联邦学习在保护隐私方面效果显著，但其系统仍面临单点故障、缺乏激励机制以及安全性不足等局限性。为应对这些挑战，区块链技术被整合到联邦学习系统中，以提供更强的安全性、公平性和可扩展性。然而，区块链赋能的联邦学习（BC-FL）系统对网络、计算和存储资源提出了额外需求。本文全面综述了关于BC-FL系统的最新研究，分析了区块链集成带来的优势与挑战。我们探讨了区块链为何适用于联邦学习、如何实现其集成，以及集成过程中的挑战与现有解决方案。此外，我们还展望了BC-FL系统未来的研究方向。