Feature reduction in federated learning for intrusion detection in IoT networks

Abstract The rapid growth of the Internet of Things (IoT) has significantly increased the complexity of device interactions, making IoT networks more vulnerable to sophisticated cyber threats. Effective intrusion detection is therefore crucial to ensuring the security and resilience of these systems. This paper presents federated learning with feature reduction (Fed-FeRe), a novel approach that enhances decentralized intrusion detection by integrating $$\chi ^{2}$$ χ 2 -based feature selection with a gated recurrent unit model. Fed-FeRe introduces an adaptive initialization of the performance threshold $$\alpha $$ α and a data-driven estimation of key h