SMoQKE-IDS: Sparse Mixture of Quantum Kolmogorov–Arnold Network Experts for FL-IDS in Edge-IIoT
Jyoti Prakash Sahoo, Binayak Kar, Yi-Leh Wu, Ahmed M. Abdelmoniem, Dimitris Chatzopoulos · IEEE Open Journal of the Communications Society · 2026
The widespread adoption of Industrial Internet of Things (IIoT) devices at the network edge necessitates advanced intrusion detection systems to mitigate sophisticated cyber threats. Edge IIoT environments, characterized by heterogeneous data and resource constraints, demand adaptive, scalable, and interpretable security solutions. This study proposes a sparse mixture of Quantum Kolmogorov-Arnold Experts Intrusion Detection system (SMoQKE-IDS), an innovative framework that integrates quantum-classical machine learning, federated learning, and interpretable AI within a Mixture of Experts (MoE) architecture. Unlike existing quantum-enhanced or Kolmogorov-Arnold Networks (KAN)-based IDS, which typically rely on standalone architectures or dense, unified ensembles, SMoQKE-IDS introduces a Sparse Mixture of Experts (SMoE) paradigm to decouple model capacity from computational overhead. The framework integrates parameterized quantum circuits for high-dimensional Hilbert-space feature mapping, convolutional layers for spatiotemporal feature extraction, and KANs for interpretable univariate nonlinear modeling. These heterogeneous modules are governed by a modality-specific sparse gating mechanism that implements conditional computation by dynamically activating the top-k optimal expert sub-networks for each input. This approach effectively mitigates the computational redundancy inherent in dense models while enhancing adaptability to diverse network traffic profiles. Additionally, federated learning enables privacy-preserving and scalable training, while the inherent interpretability of the framework enhances trust through visual and analytical transparency. Rigorous validation on benchmark datasets within federated environments demonstrates superior detection accuracy, heightened transparency, and robust performance, establishing SMoQKE-IDS as a robust solution for securing edge IIoT ecosystems.