The Internet of Medical Things (IoMT) integrates sensors, medical devices, and Internet of Things (IoT) technologies to provide data-driven healthcare systems. The systems facilitate medical monitoring and decision-making; however, there are significant concerns about data leakage and patient consent. Additionally, a shortage of large, high-quality IoMT datasets to study the surrounding issues is problematic. Federated learning (FL) is a decentralized machine learning approach that potentially offers substantial amounts of capacity, so that compound Smart Healthcare Systems (SHSs) can further personalize and contextualize the secrecy of data and strong system structures. Additionally, to protect against advanced and shifting computational intelligence-based cyber threats, especially in operational health environments, the use of Intruder Detection Systems (IDSs) is quite essential. However, traditional approaches to implementing IDSs are usually computationally costly and inappropriate for the narrow contours of deploying medical IoT devices. To address these challenges, the proposed study introduces PriFed-IDS, a novel, privacy-preserving FL-based IDS framework based on FL and reinforcement learning. The proposed model leverages reinforcement learning to uncover latent patterns in medical data, enabling accurate anomaly detection. A dynamic federation and aggregation strategy is implemented to optimize model performance while minimizing communication overhead by adaptively engaging clients in the training process. Experimental evaluations and theoretical analysis demonstrate that PriFed-IDS significantly outperforms existing benchmark IDS models in terms of detection accuracy and efficiency, underscoring its practical applicability for securing real-world IoMT networks.

The effective maintenance of Mobile Applications (MAPPs) relies on the timely and accurate triage of bug reports, where severity labels determine the order

Networked Control Systems (NCSs) have been instrumental in realizing fully connected and responsive intelligent environments within the context of real-time virtual control and management. However, traditional NCSs face considerable challenges in handling the vast amounts of data generated by large-scale control applications, particularly in terms of data acquisition, storage, and computational processing. To address these challenges, the emergence of cloud computing and advancements in control theory have empowered the new paradigm known as Cloud Control Systems (CCSs). Recently, CCSs have received substantial attention from industries for their potential properties, such as large-scale data management, complex computations, and data-centric optimized decisions. This study presents an extensive review of recent progress in CCSs spanning over multiple studies published between 2012 and 2025. Specifically, the focus is on providing a taxonomy of the current findings in CCS research, encompassing various perspectives, such as its efficient implementations in industrial automation, security and privacy considerations, and cloud-based control techniques. Each category is examined in depth through selected state-of-the-art analyses of different approaches and contrasting methodologies. Furthermore, we discuss future directions aimed at designing more efficient and practical CCSs. The insights gained from this study can help researchers, practitioners, and decision-makers in their domain for effective CCS design and deployment.