The Internet of Things under Federated Learning: A Review of the Latest Advances and Applications

With the rapid development of artificial intelligence, the Internet of Things (IoT) can deploy various machine learning algorithms for network and application management. In the IoT environment, many sensors and devices generate massive data, but data security and privacy protection have become a serious challenge. Federated learning (FL) can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing. This review aims to deeply explore the combination of FL and the IoT, and analyze the application of federated learning in the IoT from the aspects of security and privacy protection. In this paper, we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security. Next, we focus on exploring and analyzing the advantages of the combination of FL on the Internet, including privacy security, attack detection, efficient communication of the IoT, and enhanced learning quality. We also list various application scenarios of FL on the IoT. Finally, we propose several open research challenges and possible solutions.