A contrastive learning and knowledge distillation-based framework for efficient federated intrusion detection in IoT
In the era of pervasive connectivity, the widespread deployment of Internet of Things (IoT) devices across various applications has led to a rise in malicious attacks, necessitating the development of robust network intrusion detection systems (IDS) for IoT. Traditional deep learning-based IDS are c...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2025.2518963 |
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