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.

Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and Ultrasound (US)) poses additional challenges in FS, necessitating advanced feature fusion strategies. Multi-modal feature fusion combines information from different imaging modalities to improve diagnostic accuracy. Recently, quantum computing has gained attention as a revolutionary approach for FS, providing the potential to handle high-dimensional medical data more efficiently. This systematic literature review comprehensively examines classical, Deep Learning (DL), hybrid, and quantum-based FS techniques in medical imaging. Key outcomes include a structured taxonomy of FS methods, a critical evaluation of their performance across modalities, and identification of core challenges such as computational burden, interpretability, and ethical considerations. Future research directions—such as explainable AI (XAI), federated learning, and quantum-enhanced FS—are also emphasized to bridge the current gaps. This review provides actionable insights for developing scalable, interpretable, and clinically applicable FS methods in the evolving landscape of medical imaging.

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in the fourth position because of the leading death cause in its premature stages. The cervix which is the lower end of the vagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumor in the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approach uses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer to train the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of Medical Things (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardous attacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74% misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stages with the help of patient clinical records, and all medical professionals will get beneficial diagnosing approaches from this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due to cervical cancer.