用群体智能优化客户端选择，提升联邦学习在网络安全中的抗干扰能力
Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications
- 采用9种群体智能算法筛选联邦学习客户端
- 灰狼算法在多种场景下准确率、召回率和F1最高
- 适合物联网与大规模网络的入侵检测应用
本研究针对联邦学习（FL）中客户端选择的群体智能优化（SI）应用存在的空白，聚焦网络安全场景。现有研究多集中于集中式机器学习优化，忽视了去中心化联邦学习中客户端异构性、非独立同分布（non-IID）数据及对抗噪声等挑战。本文评估了九种SI算法：灰狼优化（GWO）、粒子群优化（PSO）、布谷鸟搜索、蝙蝠算法、蜂群算法、蚁群优化、鱼群算法、萤火虫算法和智能水滴算法，在四种实验场景下的表现：固定参与、动态参与、异构非独立同分布数据分布以及对抗噪声条件。结果表明，GWO在所有配置下均表现出最优的适应性与鲁棒性，准确率、召回率和F1分数最高；PSO与布谷鸟搜索也表现良好。这些发现证实了群体智能算法在应对去中心化与对抗性联邦学习挑战中的潜力，为物联网及大规模网络的入侵检测等网络安全应用提供了可扩展且鲁棒的解决方案。
原文摘要 · Abstract (English)
This study addresses a critical gap in the literature regarding the use of Swarm Intelligence Optimization (SI) algorithms for client selection in Federated Learning (FL), with a focus on cybersecurity applications. Existing research primarily explores optimization techniques for centralized machine learning, leaving the unique challenges of client diveristy, non-IID data distributions, and adversarial noise in decentralized FL largely unexamined. To bridge this gap, we evaluate nine SI algorithms-Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Cuckoo Search, Bat Algorithm, Bee Colony, Ant Colony Optimization, Fish Swarm, Glow Worm, and Intelligent Water Droplet-across four experimental scenarios: fixed client participation, dynamic participation patterns, hetergeneous non-IID data distributions, and adversarial noise conditions. Results indicate that GWO exhibits superior adaptability and robustness, achieving the highest accuracy, recall and F1-scoress across all configurations, while PSO and Cuckoo Search also demonstrate strong performance. These findings underscore the potential of SI algorithms to address decentralized and adversarial FL challenges, offereing scalable and resilient solutions for cybersecurity applications, including intrusion detection in IoT and large-scale networks.
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