用知识蒸馏提升物联网入侵检测的联邦学习稳定性与效率
FedTransKD-IDS: Robust Federated Transfer Learning with Knowledge Distillation for Intrusion Detection in IoT
- 通过几何均值聚合+迁移学习+知识蒸馏，增强联邦学习鲁棒性
- 在异构数据上实现99.18%准确率与99.99%召回率
- 适合资源受限边缘节点的隐私保护型入侵检测场景
在现代分布式网络环境，尤其是物联网架构和5G网络中，严格的隐私保护与可扩展性要求给入侵检测系统带来挑战。尽管联邦学习通过避免数据集中化来保护隐私，但在边缘节点存在严重统计异构性和资源约束时，其效率与稳定性显著下降。为此，本文提出FedTransKD-IDS框架，通过基于几何平均的鲁棒聚合、联邦迁移学习与知识蒸馏相结合，提升系统稳定性和效率。协作训练的全局教师模型将特征提取部分传递给轻量级学生模型。在异构数据集上的实验表明，该方法达到最高99.18%的准确率和99.99%的召回率，验证了结构化知识迁移在联邦环境中的有效性。
原文摘要 · Abstract (English)
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.
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