Dual Attention-Based Federated Learning for Wireless Traffic Prediction

TL;DR

This paper introduces FedDA, a novel federated learning framework with dual attention for wireless traffic prediction, improving accuracy while preserving data privacy and addressing heterogeneity.

Contribution

The paper proposes a dual attention-based federated learning framework that enhances wireless traffic prediction by effectively handling data heterogeneity and preserving privacy.

Findings

FedDA outperforms existing methods in real-world datasets.

Achieves up to 30% reduction in mean squared error.

Effectively captures diverse traffic patterns with dual attention.

Abstract

Wireless traffic prediction is essential for cellular networks to realize intelligent network operations, such as load-aware resource management and predictive control. Existing prediction approaches usually adopt centralized training architectures and require the transferring of huge amounts of traffic data, which may raise delay and privacy concerns for certain scenarios. In this work, we propose a novel wireless traffic prediction framework named \textit{Dual Attention-Based Federated Learning} (FedDA), by which a high-quality prediction model is trained collaboratively by multiple edge clients. To simultaneously capture the various wireless traffic patterns and keep raw data locally, FedDA first groups the clients into different clusters by using a small augmentation dataset. Then, a quasi-global model is trained and shared among clients as prior knowledge, aiming to solve the statistical heterogeneity challenge confronted with federated learning. To construct the global model, a dual attention scheme is further proposed by aggregating the intra- and inter-cluster models, instead of simply averaging the weights of local models. We conduct extensive experiments on two real-world wireless traffic datasets and results show that FedDA outperforms state-of-the-art methods. The average mean squared error performance gains on the two datasets are up to 10\% and 30\%, respectively.