Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning
This work addresses privacy and robustness issues in network traffic prediction for intelligent network operations, though it appears incremental as it builds on federated learning with differential privacy.
The paper tackles the challenge of Byzantine attacks in federated learning for cellular traffic prediction by proposing an asynchronous differential federated learning framework with distributionally robust optimization and regularization, achieving superior performance over existing methods on three real-world datasets.
Network traffic prediction plays a crucial role in intelligent network operation. Traditional prediction methods often rely on centralized training, necessitating the transfer of vast amounts of traffic data to a central server. This approach can lead to latency and privacy concerns. To address these issues, federated learning integrated with differential privacy has emerged as a solution to improve data privacy and model robustness in distributed settings. Nonetheless, existing federated learning protocols are vulnerable to Byzantine attacks, which may significantly compromise model robustness. Developing a robust and privacy-preserving prediction model in the presence of Byzantine clients remains a significant challenge. To this end, we propose an asynchronous differential federated learning framework based on distributionally robust optimization. The proposed framework utilizes multiple clients to train the prediction model collaboratively with local differential privacy. In addition, regularization techniques have been employed to further improve the Byzantine robustness of the models. We have conducted extensive experiments on three real-world datasets, and the results elucidate that our proposed distributed algorithm can achieve superior performance over existing methods.