Federated Learning Model Training Mechanism with Edge Cloud Collaboration for Services in Smart Cities

Abstract

With the development of big data and artificial intelligence, problems related to data privacy have emerged in smart cities. In the context of large-scale data, federated learning can effectively utilize data resources and ensure user data privacy. This paper designs a training mechanism of edge cloud collaborative federated learning model for smart city applications, so that the model training is carried out on the edge side, without the need to gather the original data set to the cloud computing center, to ensure the privacy and security of data. Finally, it is verified and tested in the vehicle recognition scene in the traffic field. The results show that this mechanism has certain advantages in detecting delay and protecting privacy.