5G Traffic Forecasting using Federated Learning

The Internet of Things (IoT) and other mobile services stand to benefit greatly from the deployment of 5G networks as an enabling infrastructure. However, 5G networks confront several challenges, the most significant of which is the huge volumes of data created by these networks, data security, and privacy concerns. A possible strategy for resolving these challenges in 5G networks is referred to as Federated Learning (FL). However, the accuracy of the predictive models is critically important. In this paper, we propose a 5G traffic forecasting approach using federated learning. Our approach leverages the benefits of FL to enable accurate traffic forecasting in a 5G network while preserving user privacy. We evaluate the performance of our proposed approach on a real-world dataset and compare it with traditional traffic forecasting methods. The results show that our approach outperforms traditional methods in terms of prediction accuracy and efficiency. The use of FL makes it possible to train models using data that is dispersed over several devices or places while still protecting the confidentiality and integrity of the data. Our proposed model is highly accurate for 5G traffic forecasting against State of art approaches.