FedTransKD-IDS: Robust Federated Transfer Learning with Knowledge Distillation for Intrusion Detection in IoT
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 di...
| Authors | Mohammad Hosssein Gholamrezazadeh, Ahmadreza MontazerolghaemAhmadreza Montazerolghaem |
|---|---|
| Primary category | cs.NI |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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