Lightweight Federated-Learning-Driven Traffic Prediction for Heterogeneous IoT Networks

With the rapid development of the Internet of Things (IoT), more and more IoT traffic is generated in the data network. Accurate perception of IoT traffic changes will facilitate traffic engineering decisions, thus ensuring the performance of IoT applications. However, current traffic prediction methods ignore the limitations of actual application environment. In this article, we propose an IoT traffic prediction method based on horizontal federated learning to predict traffic trends under the cooperation of the cloud and the edge side. In order to improve the accuracy of IoT traffic prediction, a traffic prediction model SMN3-CIFGA is proposed to predict IoT traffic based on traffic feature extraction in a limited hardware environment. In addition, in order to improve the communication efficiency in the distributed training process of the traffic prediction model, we propose a gradient compression algorithm based on dynamic threshold (GCADT). The experimental results demonstrate that compared with current methods, the average training time of the GCADT algorithm is reduced by about 6.21%, the transmission gradient size of the GCADT is reduced by about 66.71%, the average training time of the classification model SMN3 is reduced by about 40%, and the testing set prediction accuracy of SMN3-CIFGA can reach 97.61%.