Federated Learning-Driven Edge AI for Enhanced Mobile Traffic Prediction

The recent surge in mobile traffic has increasingly underscored the importance of Edge AI. The Edge Server (ESs) in Edge AI facilitate precise traffic prediction by collecting regional data and analyzing the characteristics and traffic patterns of adjacent areas. However, existing Edge AI systems for mobile traffic prediction are limited by their reliance on physical proximity for regional selection, failing to effectively leverage the unique infrastructure and lifestyle patterns of each area. This study proposes a novel Edge AI mobile traffic prediction architecture that overcomes the performance limitations of traditional methods by integrating multi Temporal Convolutional Networks-Long Short Term Memory (TCN-LSTM) with clustering techniques that reflect regional characteristics. The proposed approach is unconstrained by distances between regions, hence maximally utilizing unique features of each area. Furthermore, by incorporating Federated Learning (FL), this study significantly reduces the computational load, optimizing the model for real-world applications. The effectiveness of this model is validated across various Edge AI scenarios of different sizes, demonstrating a performance improvement of approximately 30% in Mean Absolute Percentage Error (MAPE) compared to conventional Edge AI system.