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.

翻译：随着机器学习技术的进步，处理现实世界中产生的海量数据已有有效方法。然而，隐私和可扩展性问题将制约机器学习的发展。联邦学习（FL）通过将训练任务分配给多个客户端，使中央服务器与本地设备分离，从而防止隐私泄露。然而，联邦学习仍存在单点故障和恶意数据等缺陷。区块链的出现为联邦学习的部署提供了安全高效的解决方案。本文对区块链联邦学习（BCFL）的相关文献进行了全面综述。首先，我们从系统组成的角度探讨了区块链如何应用于联邦学习。接着，从机制设计的角度分析了BCFL的具体功能，并阐明了区块链针对联邦学习解决了哪些具体问题。我们还综述了BCFL在现实中的应用。最后，讨论了当前面临的挑战及未来的研究方向。