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    Federated Deep Learning for collaborative intrusion detection in heterogeneous networks

    Popoola, Segun I, Gui, Guan, Adebisi, Bamidele ORCID logoORCID: https://orcid.org/0000-0001-9071-9120, Hammoudeh, Mohammad ORCID logoORCID: https://orcid.org/0000-0002-9735-2365 and Gacanin, Haris (2021) Federated Deep Learning for collaborative intrusion detection in heterogeneous networks. In: 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 27 September 2021 - 30 September 2021, Norman, OK, USA.

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    In this paper, we propose Federated Deep Learning (FDL) for intrusion detection in heterogeneous networks. Local Deep Neural Network (DNN) models are used to learn the hierarchical representations of the private network traffic data in multiple edge nodes. A dedicated central server receives the parameters of the local DNN models from the edge nodes, and it aggregates them to produce an FDL model using the Fed+ fusion algorithm. Simulation results show that the FDL model achieved an accuracy of 99.27 ± 0.79%, a precision of 97.03 ± 4.22%, a recall of 98.06 ± 1.72%, an F1 score of 97.50 ± 2.55%, and a False Positive Rate (FPR) of 2.40 ± 2.47%. The classification performance and the generalisation ability of the FDL model are better than those of the local DNN models. The Fed+ algorithm outperformed two state-of-the-art fusion algorithms, namely federated averaging (FedAvg) and Coordinate Median (CM). Therefore, the DNN-Fed+ model is preferable for intrusion detection in heterogeneous wireless networks.

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