Federated learning plays an important role in the process of smart cities. With the development of big data and artificial intelligence, there is a problem of data privacy protection in this process. Federated learning is capable of solving this problem. This paper starts with the current developments of federated learning and its applications in various fields. We conduct a comprehensive investigation. This paper summarize the latest research on the application of federated learning in various fields of smart cities. In-depth understanding of the current development of federated learning from the Internet of Things, transportation, communications, finance, medical and other fields. Before that, we introduce the background, definition and key technologies of federated learning. Further more, we review the key technologies and the latest results. Finally, we discuss the future applications and research directions of federated learning in smart cities.

翻译：联邦学习在智能城市进程中起着重要作用。随着大数据和人工智能的发展,在这个过程中存在数据隐私保护问题。联邦学习能够解决这一问题。本文以目前联邦学习的发展及其在各个领域的应用为起点。我们进行全面调查。本文件总结了关于在智能城市各个领域应用联邦学习的最新研究。深入了解目前从Thing、交通、通信、金融、医疗和其他领域的互联网上联合会学习的发展情况。在此之前,我们介绍联邦学习的背景、定义和关键技术。此外,我们审查关键技术和最新成果。最后,我们讨论智能城市中联邦学习的未来应用和研究方向。