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HFedSNN: Efficient Hierarchical Federated Learning using Spiking Neural Networks.

, , and . MobiWac, page 53-60. ACM, (2023)

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Federated Semisupervised Learning for Attack Detection in Industrial Internet of Things., , , and . IEEE Trans. Ind. Informatics, 19 (1): 286-295 (2023)Incentive techniques for the Internet of Things: A survey., , , , , , , and . J. Netw. Comput. Appl., (2022)Decision tree-based blending method using deep-learning for network management., , and . NOMS, page 1-7. IEEE, (2022)Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions., , , , , , , and . CoRR, (2021)Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities., , , , and . Future Internet, 15 (12): 377 (2023)A Semi-supervised Stacked Autoencoder Approach for Network Traffic Classification., , and . ICNP, page 1-6. IEEE, (2020)F-BIDS: Federated-Blending based Intrusion Detection System., and . Pervasive Mob. Comput., (February 2023)Ensemble-Based Deep Learning Model for Network Traffic Classification., , and . IEEE Trans. Netw. Serv. Manag., 19 (4): 4124-4135 (December 2022)Machine Learning-Enabled Network Traffic Analysis. (Analyse du trafic réseau basée sur l'apprentissage automatique).. University of Nantes, France, (2022)HFedSNN: Efficient Hierarchical Federated Learning using Spiking Neural Networks., , and . MobiWac, page 53-60. ACM, (2023)