通过自适应客户端选择，大幅降低联邦学习通信开销，提升网络异常检测效率。
Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
- 结合批量优化与动态客户端选择，实现异步更新。
- 通信时间从700秒降至16.8秒，准确率保持在95.10%。
- 适合资源受限场景下的实时异常检测系统部署。
联邦学习（FL）在网络安全异常检测中面临通信开销大的挑战，尤其在客户端配置多样、网络条件不一的情况下，影响效率与检测精度。现有方法各自优化，难以兼顾性能与低开销。本文提出一种自适应框架，融合批量大小优化、客户端选择与异步更新，用于高效异常检测。在UNSW-NB15通用网络流量和ROAD车载网络数据集上，通信开销降低97.6%（700.0秒→16.8秒），准确率保持95.10%（原为95.12%）。曼-惠特尼U检验显示结果具有统计显著性（p < 0.05）。性能剖析表明，该框架通过减少GPU计算与内存传输提升了效率，可在不同客户端条件下保持鲁棒检测能力。
原文摘要 · Abstract (English)
Communication overhead in federated learning (FL) poses a significant challenge for network anomaly detection systems, where diverse client configurations and network conditions impact efficiency and detection accuracy. Existing approaches attempt optimization individually but struggle to balance reduced overhead with performance. This paper presents an adaptive FL framework combining batch size optimization, client selection, and asynchronous updates for efficient anomaly detection. Using UNSW-NB15 for general network traffic and ROAD for automotive networks, our framework reduces communication overhead by 97.6% (700.0s to 16.8s) while maintaining comparable accuracy (95.10% vs. 95.12%). The Mann-Whitney U test confirms significant improvements (p < 0.05). Profiling analysis reveals efficiency gains via reduced GPU operations and memory transfers, ensuring robust detection across varying client conditions.
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