# Wireless Traffic Prediction Based on a Gradient Similarity Federated Aggregation Algorithm
## 3 Proposed Framework and Methods
### Global Aggregation Based on Gradient Similarity
Therefore, in this subsection, we report the prediction accuracy along with each communication cycle (epoch) and use the R-squared fraction to indicate the accuracy as it reflects how well the model predicts the true value of the network traffic [21]. As shown in Figure 8, we can observe that FedGSA achieves a higher prediction accuracy on both datasets, in addition to the fact that FedGSA requires fewer communication rounds to reach a certain accuracy; for example, for the Milano dataset, after 30 communication rounds, FedGSA achieves an accuracy of about 82% for wireless network traffic, and as for FedAVG, the prediction accuracy is about 75% Therefore, we consider that our proposed method has higher communication efficiency, which is also one of the important metrics for evaluating federation learning methods. ## 5 Conclusions

In this paper, we propose a model gradient-based similarity aggregation scheme for federation learning and wireless network traffic prediction and name the framework

Figure 8: (**a**) Prediction accuracy and communication rounds on the Trento dataset. (**b**) Prediction accuracy and communication rounds on the Milano dataset. Figure 7: (**a**) describes the traffic comparison on the Milano dataset; (**b**) describes the results of the cumulative distribution function (CDF) on the Milano dataset. FedGSA, which uses similarity knowledge to construct individualized models for each client and realize model aggregation for each client to improve the generalization ability of the final global model. Experiments are conducted to predict the base station network traffic based on LSTM long short-term memory network on two real network traffic datasets, and the enhanced data scheme and sliding window strategy are combined to further overcome the problem of high data heterogeneity during FL training and improve the prediction capability. Compared with the current mainstream federal average algorithm, the method proposed in this paper achieves good results in simulation experiments. L.L. : methodology, formal analysis, data curation and validation and writing--original draft. Y.Z. : methodology, writing--review and editing and project administration. J.W. : Funding acquisition and Supervision. C.Z. : conceptualization, methodology and formal analysis. All authors have read and agreed to the published version of the manuscript. This work was supported by Natural Science Foundation of Inner Mongolia Province of China (Grant No. 2022 MS06006, 2020 MS06009) and supported by Basic research funds for universities of Inner Mongolia Province of China (Grant Name. Application of Federated Learning for Privacy protection in wireless traffic prediction). Not applicable. The data in the study are from the literature, which is publicly accessible. The authors declare no conflict of interest. ## References

* (1) [PERSON]; [PERSON]; [PERSON]; [PERSON] Artificial intelligence defined 5G radio access networks. _IEEE Commun. Mag._**2019**, _57_, 14-20. [CrossRef]
* (2) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Wireless traffic prediction with scalable Gaussian process: Framework, algorithms, and verification. _IEEE J. Sel. Areas Commun._**2019**, _37_, 1291-1306. [CrossRef]
* (3) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Ten challenges in advancing machine learning technologies toward 6G. _IEEE Wirel. Commun._**2020**, _27_, 96-103. [CrossRef]
* (4) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20 th International Conference Artificial Intelligence and Statistics, Ft. Lauderdale, FL, USA, 20-22 April 2017; pp. 1273-1282. * (5) [PERSON]; [PERSON]; [PERSON]; [PERSON] Dual attention-based federated learning for wireless traffic prediction. In Proceedings of the IEEE INFOCOM 2021-IEEE Conference on Computer Communications, Virtual, 10-13 May 2021; pp. 1-10. * (6) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Wireless traffic modeling and prediction using seasonal arima models. _IEICE Trans.