Dual Attention-Based Federated Learning for Wireless Traffic Prediction

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 tra...

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Veröffentlicht in:Annual Joint Conference of the IEEE Computer and Communications Societies S. 1 - 10
Hauptverfasser: Zhang, Chuanting, Dang, Shuping, Shihada, Basem, Alouini, Mohamed-Slim
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 10.05.2021
Schlagworte:
Collaborative work
Computational modeling
deep neural networks
federated learning
Performance gain
Predictive models
Traffic control
Training
Wireless communication
wireless traffic prediction
ISSN:2641-9874
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Zusammenfassung: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 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.
ISSN:2641-9874
DOI:10.1109/INFOCOM42981.2021.9488883