FedTransKD-IDS: Robust Federated Transfer Learning with Knowledge Distillation for Intrusion Detection in IoT

2026-08-06

AI Summary

This work addresses the challenges posed by high data heterogeneity, limited edge resources, and privacy sensitivity in distributed networks such as the Internet of Things, which degrade the efficiency and stability of conventional federated learning. To overcome these limitations, the authors propose FedTransKD-IDS, a novel framework that synergistically integrates geometric-mean-based robust aggregation, federated transfer learning, and knowledge distillation. This approach effectively transfers the feature extraction capability of a global teacher model to lightweight student models, enabling efficient and structured knowledge dissemination in federated settings. Experimental results demonstrate that the proposed framework achieves 99.18% accuracy and 99.99% recall on heterogeneous datasets, significantly enhancing intrusion detection performance, model stability, and generalization capability.

Abstract

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.

Problem

• Federated Learning
• Intrusion Detection
• Statistical Heterogeneity
• Privacy Preservation
• IoT Security

Innovation

• Federated Transfer Learning
• Knowledge Distillation
• Robust Aggregation
• Intrusion Detection System
• IoT Security