# Wireless Traffic Prediction Based on a Gradient Similarity Federated Aggregation Algorithm
## 1 Introduction  
According to the State Ministry of Industry and Information Technology, as of the end of August 2022, 1,854,000 5G base stations have been completed and opened nationwide. Indeed, 5G networks play an essential role in realizing application scenarios such as AR, Telematics, and 4K TV, but 5G base station construction also faces problems such as difficulty and long investment cycles, etc. If we can accurately understand the different demands and growth trends of network traffic in each region, we can allocate network resources and reasonably plan the construction of 5G base stations. Meanwhile, the development and application of big data and artificial intelligence technologies are very effective in improving the quality of service (QoS) of access and core networks [1]. The application of artificial intelligence in the convergence of communication networks is significant for the accurate prediction of wireless traffic. Wireless traffic prediction estimates future traffic data volumes based on historical data and provides a decision basis for communication network management and optimization [2], and, based on the predicted traffic data, proactive measures can be taken to alleviate network congestion and improve network operation performance. In addition, common heterogeneous service requirements can be well met in future 6G communication networks by wireless traffic prediction at a lower cost [3]. Existing centralized traffic prediction methods require large and frequent interactions to share data from various regions for learning prediction. In real-world applications, it is difficult to achieve sufficient data sharing across enterprises due to multi-level privacy factors, i.e., the existence of data silos. A large number of data interactions also poses a huge communication overhead and risk of privacy leakage, and the centralized training data model poses a huge challenge to the computational and storage capacity of the central server. The implementation and application of Federal Learning (FL) provide a new way of thinking for traffic prediction models. Specifically, FL provides a distributed training architecture that can be jointly applied with many machine learning algorithms, especially deep neural networks, based on which local data can be effectively learned and global models can be obtained by iteratively aggregating local training models, which can also share the data information of the clients while protecting the privacy of the training client data, thus obtaining more accurate prediction results. FL has been studied in the field of wireless traffic prediction, but there are still many challenges and problems. First, the client data of collaborative learning in FL has certain heterogeneity, i.e., Non-Independent Identically Distribution (Non-IID) characteristics, and the effective solution of the data heterogeneity problem is a prerequisite for the effective execution of the federal learning algorithm. In addition, the generalization performance of the global model generated by the final aggregation in FL traffic prediction largely determines the model prediction capability. In 2017, the federal average [4] algorithm proposed by Google uses the average aggregation approach for the integration of model parameters across edge nodes, but the strategy does not consider the differences between edge computing nodes, and the average global aggregation weights will undoubtedly reduce the generalization effect of the global model. Based on the above problems, this paper proposes a new wireless network traffic prediction method, called Federated Gradient Similarity-based Aggregation Algorithm for Wireless Traffic Prediction (FedGSA), which can collaboratively train multiple base stations and provide them with high-quality prediction models, including an enhanced data strategy based on global incremental integration, a two-channel training data scheme using sliding window construction, and a gradient aggregation mechanism to cope with data heterogeneity and global model generalization in FL.