Intrusion detection based on privacy-preserving federated learning for the industrial IoT
P Ruzafa-Alcázar, P Fernández-Saura… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Federated learning (FL) has attracted significant interest given its prominent advantages and
applicability in many scenarios. However, it has been demonstrated that sharing updated
gradients/weights during the training process can lead to privacy concerns. In the context of
the Internet of Things (IoT), this can be exacerbated due to intrusion detection systems
(IDSs), which are intended to detect security attacks by analyzing the devices' network traffic.
Our work provides a comprehensive evaluation of differential privacy techniques, which are …
applicability in many scenarios. However, it has been demonstrated that sharing updated
gradients/weights during the training process can lead to privacy concerns. In the context of
the Internet of Things (IoT), this can be exacerbated due to intrusion detection systems
(IDSs), which are intended to detect security attacks by analyzing the devices' network traffic.
Our work provides a comprehensive evaluation of differential privacy techniques, which are …
[CITATION][C] Intrusion Detection based on Privacy-preserving Federated Learning for the Industrial IoT
P RUZAFA, SP FERNÁNDEZ… - IEEE … - publications.jrc.ec.europa.eu
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