# Wireless Traffic Prediction Based on a Gradient Similarity Federated Aggregation Algorithm
## 2 Related Work
### Wireless Traffic Prediction  
Accurate traffic modeling and forecasting capabilities play an important role in wireless services, and research related to wireless traffic forecasting has received significant attention. Wireless traffic prediction is essentially a time series prediction problem. The solution methods can be broadly classified into three categories, namely, simplex methods, parametric methods, and non-parametric methods.  
The historical average method and the simplex method are the representatives of the first type of method. The two methods use the average value of historical data and the last observation as the prediction value, respectively. This type of prediction method does not require complex calculations and is easy to implement, but it cannot capture the hidden patterns of the wireless traffic and the prediction performance is relatively poor.  
For the second category, i.e., parametric methods, tools based on statistics and probability theory are used to model and forecast wireless services, among which the most classical method is the Auto-Regressive Integrated Moving Average (ARIMA). In order to characterize the self-similarity and burstiness of wireless traffic, the authors explored ARIMA and its variants in the literature [6; 7]. In addition to ARIMA models, literature [8; 9; 10] explored alpha-stable models, entropy theory, and covariance functions to perform wireless traffic forecasting, respectively.  
With the rapid development of machine learning and artificial intelligence techniques, nonparametric methods have become a strong contender among wireless traffic prediction methods. In particular, research on wireless traffic prediction based on deep neural networks has attracted great attention. In the literature [11], the authors designed a traffic prediction model based on a multi-channel sparse long-term short-term memory network to capture multi-source network traffic information and improve the ability of deep neural network models to capture important features. In [12], the authors designed a Generative Adversarial Network (GAN) traffic prediction method and separately captured traffic spatio-temporal features and base-station-type features, input the spliced features into the composite residual module to generate predicted traffic, judge the generated traffic by the discriminative network, and then generate highly accurate predicted traffic by the generative network after the game confrontation between the generative network and the discriminative network.  
To effectively extract spatial and temporal features, a joint spatio-temporal prediction model based on neural networks has been proposed in the literature [13], which uses graph convolutional networks to extract complex topological spatial features in a targeted manner, while using gated cyclic units to extract temporal features of the traffic. City-scale wireless traffic forecasting is also studied in the literature [14], where the authors introduce a new forecasting framework by modeling the spatio-temporal correlation of cross-domain datasets.  
The above work mainly uses centralized wireless traffic prediction, and to address the problems of communication overhead, privacy leakage, and data silos in centralized prediction schemes, this paper implements wireless traffic prediction through distributed architecture and federated learning.