Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning

TL;DR

This paper introduces a robust federated learning framework for cellular traffic prediction that enhances privacy and resilience against Byzantine attacks using asynchronous training and distributionally robust optimization.

Contribution

It proposes a novel asynchronous differential federated learning method with regularization for Byzantine robustness in traffic prediction tasks.

Findings

Achieves superior prediction accuracy compared to existing methods.

Demonstrates robustness against Byzantine client attacks.

Ensures data privacy through local differential privacy mechanisms.

Abstract

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.