发表机构
University of Isfahan（伊斯法罕大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对物联网入侵检测场景，提出FedTransKD-IDS框架，融合几何均值鲁棒聚合、联邦迁移学习与知识蒸馏，在异质数据集上实现99.18%准确率、99.99%召回率，提升了联邦学习的稳定性与效率。
AI 中文摘要
在现代分布式网络环境，尤其是物联网基础设施与5G网络中，严格的隐私保护和可扩展性要求给入侵检测系统带来了重大挑战。尽管联邦学习通过阻止数据集中化保护隐私，但在边缘节点严重的统计异质性和资源约束下，其效率与稳定性会大幅下降。为解决这些局限，本研究引入FedTransKD-IDS框架，该框架通过整合基于几何均值的鲁棒聚合、联邦迁移学习及知识蒸馏，提升系统稳定性与效率。在该框架内，协同训练的全局教师模型将其特征提取组件迁移至轻量级学生模型。对异质数据集的实验评估显示，其检测性能达到峰值，准确率为99.18%、召回率为99.99%，表明联邦环境下结构化知识迁移的有效性。
英文摘要
In modern distributed network environments, particularly in Internet of Things infrastructures and 5G networks, stringent privacy preservation and scalability requirements have created significant challenges for intrusion detection systems. Although federated learning preserves privacy by preventing data centralization, its efficiency and stability is considerably degraded under severe statistical heterogeneity and resource constraints of edge nodes. To address these limitations, this study introduces the FedTransKD-IDS framework, which enhances both system stability and efficiency by integrating robust aggregation based on the geometric mean, federated transfer learning, and knowledge distillation. Within this framework, the collaboratively trained global teacher model transfers its feature extraction component to lightweight student models. Experimental evaluation on heterogeneous datasets demonstrates a peak detection performance, achieving an accuracy of 99. 18% and a recall of 99. 99%, thereby indicating the effectiveness of structured knowledge transfer in federated environments.
