FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT Environments
With the rapid advancement of Internet of Things (IoT) technology, intrusion detection systems (IDSs) have become pivotal in ensuring network security. However, the data produced by IoT devices is typically sensitive and tends to display non-independent and identically distributed (Non-IID) properti...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4309 |
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| author | Haonan Peng Chunming Wu Yanfeng Xiao |
| author_facet | Haonan Peng Chunming Wu Yanfeng Xiao |
| author_sort | Haonan Peng |
| collection | DOAJ |
| description | With the rapid advancement of Internet of Things (IoT) technology, intrusion detection systems (IDSs) have become pivotal in ensuring network security. However, the data produced by IoT devices is typically sensitive and tends to display non-independent and identically distributed (Non-IID) properties. These factors impose significant limitations on the application of traditional centralized learning. In response to these issues, this study introduces a novel IDS framework grounded in federated learning and knowledge distillation (KD), termed FD-IDS. The proposed FD-IDS aims to tackle issues related to safeguarding data privacy and distributed heterogeneity. FD-IDS employs mutual information for feature selection to enhance training efficiency. For Non-IID data scenarios, the system combines a proximal term with KD. The proximal term restricts the deviation between local and global models, while KD utilizes the global model to steer the training process of local models. Together, these mechanisms effectively alleviate the problem of model drift. Experiments conducted on both the Edge-IIoT and N-BaIoT datasets demonstrate that FD-IDS achieves promising detection performance across multiple evaluation metrics. |
| format | Article |
| id | doaj-art-3a57f08e71c44ebca96049bc08a2dfe2 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3a57f08e71c44ebca96049bc08a2dfe22025-08-20T03:32:33ZengMDPI AGSensors1424-82202025-07-012514430910.3390/s25144309FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT EnvironmentsHaonan Peng0Chunming Wu1Yanfeng Xiao2College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310027, ChinaChina Mobile Group Huzhou Co., Ltd., Huzhou 313098, ChinaWith the rapid advancement of Internet of Things (IoT) technology, intrusion detection systems (IDSs) have become pivotal in ensuring network security. However, the data produced by IoT devices is typically sensitive and tends to display non-independent and identically distributed (Non-IID) properties. These factors impose significant limitations on the application of traditional centralized learning. In response to these issues, this study introduces a novel IDS framework grounded in federated learning and knowledge distillation (KD), termed FD-IDS. The proposed FD-IDS aims to tackle issues related to safeguarding data privacy and distributed heterogeneity. FD-IDS employs mutual information for feature selection to enhance training efficiency. For Non-IID data scenarios, the system combines a proximal term with KD. The proximal term restricts the deviation between local and global models, while KD utilizes the global model to steer the training process of local models. Together, these mechanisms effectively alleviate the problem of model drift. Experiments conducted on both the Edge-IIoT and N-BaIoT datasets demonstrate that FD-IDS achieves promising detection performance across multiple evaluation metrics.https://www.mdpi.com/1424-8220/25/14/4309intrusion detectionIoT securityfederated learningknowledge distillationNon-IIDfeature selection |
| spellingShingle | Haonan Peng Chunming Wu Yanfeng Xiao FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT Environments Sensors intrusion detection IoT security federated learning knowledge distillation Non-IID feature selection |
| title | FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT Environments |
| title_full | FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT Environments |
| title_fullStr | FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT Environments |
| title_full_unstemmed | FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT Environments |
| title_short | FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT Environments |
| title_sort | fd ids federated learning with knowledge distillation for intrusion detection in non iid iot environments |
| topic | intrusion detection IoT security federated learning knowledge distillation Non-IID feature selection |
| url | https://www.mdpi.com/1424-8220/25/14/4309 |
| work_keys_str_mv | AT haonanpeng fdidsfederatedlearningwithknowledgedistillationforintrusiondetectioninnoniidiotenvironments AT chunmingwu fdidsfederatedlearningwithknowledgedistillationforintrusiondetectioninnoniidiotenvironments AT yanfengxiao fdidsfederatedlearningwithknowledgedistillationforintrusiondetectioninnoniidiotenvironments |