提出抗拜占庭攻击的异步联邦学习框架，提升蜂窝网络流量预测隐私与鲁棒性。
Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning
- 基于分布鲁棒优化设计异步联邦学习框架，结合本地差分隐私
- 在三个真实数据集上性能优于现有方法，有效抵御恶意客户端干扰
- 适合关注隐私保护与模型安全的网络管理与智能运维研究者
网络流量预测在智能网络运维中至关重要。传统方法依赖集中式训练，需将大量流量数据传输至中心服务器，易引发延迟与隐私问题。为此，融合差分隐私的联邦学习成为分布式场景下提升数据隐私与模型鲁棒性的解决方案。然而，现有联邦学习协议易受拜占庭攻击影响，严重削弱模型可靠性。为应对这一挑战，本文提出一种基于分布鲁棒优化的异步差分联邦学习框架，通过多个客户端协同训练预测模型，并引入本地差分隐私机制。同时，采用正则化技术进一步增强模型对拜占庭客户端的鲁棒性。在三个真实世界数据集上的实验表明，所提分布式算法在性能上显著优于现有方法。
原文摘要 · Abstract (English)
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.
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