Interpretable Federated Learning Model for Cyber Intrusion Detection in Smart Cities with Privacy-Preserving Feature Selection

The rapid evolution of smart cities through IoT, cloud computing, and connected infrastructures has significantly enhanced sectors such as transportation, healthcare, energy, and public safety, but also increased exposure to sophisticated cyber threats. The diversity of devices, high data volumes, and real-time operational demands complicate security, requiring not just robust intrusion detection but also effective feature selection for relevance and scalability. Traditional Machine Learning (ML) based Intrusion Detection System (IDS) improves detection but often lacks interpretability, limiting stakeholder trust and timely responses. Moreover, centralized feature selection in conventional IDS compromises data privacy and fails to accommodate the decentralized nature of smart city infrastructures. To address these limitations, this research introduces an Interpretable Federated Learning (FL) based Cyber Intrusion Detection model tailored for smart city applications. The proposed system leverages privacy-preserving feature selection, where each client node independently identifies top-ranked features using ML models integrated with SHAP-based explainability. These local feature subsets are then aggregated at a central server to construct a global model without compromising sensitive data. Furthermore, the global model is enhanced with Explainable AI (XAI) techniques such as SHAP and LIME, offering both global interpretability and instance-level transparency for cyber threat decisions. Experimental results demonstrate that the proposed global model achieves a high detection accuracy of 98.51%, with a significantly low miss rate of 1.49%, outperforming existing models while ensuring explainability, privacy, and scalability across smart city infrastructures.