FLITC: A Novel Federated Learning-Based Method for IoT Traffic Classification
Author:
Affiliation:
1. University of Salamanca,Salamanca,Spain
2. NTNU,Trondheim,Norway
3. Fraunhofer IIS,Erlangen,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9820747/9820748/09821087.pdf?arnumber=9821087
Reference21 articles.
1. A federated learning approach for mobile packet classification;bakopoulou;ArXiv Preprint,2019
2. Cross-Silo Model-Based Secure Federated Transfer Learning for Flow-Based Traffic Classification
3. A federated semi-supervised learning approach for network traffic classification;peng;ArXiv Preprint,2021
4. Edge Device Identification Based on Federated Learning and Network Traffic Feature Engineering
5. A distributed traffic classification model based on Federated Learning
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2. Incremental federated learning for traffic flow classification in heterogeneous data scenarios;Neural Computing and Applications;2024-08-12
3. A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset;2024 26th International Conference on Advanced Communications Technology (ICACT);2024-02-04
4. IoT Network Intrusion Detection Using Federated Learning;Lecture Notes in Networks and Systems;2024
5. Unleashing the Potential of Knowledge Distillation for IoT Traffic Classification;IEEE Transactions on Machine Learning in Communications and Networking;2024
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