The rapid development of network communication along with the drastic increase in the
number of smart devices has triggered a surge in network traffic, which can contain private
data and in turn affect user privacy. Recently, Federated Learning (FL) has been proposed
in Intrusion Detection Systems (IDS) to ensure attack detection, privacy preservation, and
cost reduction, which are crucial issues in traditional centralized machine-learning-based
IDS. However, FL-based approaches still exhibit vulnerabilities that can be exploited by …