Internet of Things (IoT) in extreme environments inevitably produces incomplete multi-view data, presenting challenges to the existing data analysis methods. Although incomplete multi-view clustering methods have the potential to mine patterns of incomplete IoT data, they are still confronted with two challenges. 1) They ignore shifts of semantics caused by missing data in aggregating consistent and complementary information of incomplete data, degrading the robustness of models in pattern mining. 2) Most of them rely on the instances with complete views as pairwise supervision to capture correlations among views, failing to mine inherent patterns of data in the extreme view missing scenario where multi-view instances are only with an available view. To this end, a deep incomplete multi-view clustering network (DIMC) is proposed via defining dual consistencies within the information bottleneck framework to mine accurate patterns of incomplete data. Specifically, an unsupervised multi-view information bottleneck (MIB) is formulated to model dependencies of data, which remedies shifts of semantics via within-view intrinsic knowledge learning, consistent semantics sharing, and consistent structure aligning. Meanwhile, dual consistencies are designed to implement MIB, which builds invariant transformations to mine correlations between views without the help of complete instances. Finally, extensive experiments on four benchmark incomplete datasets demonstrate the superiority of DIMC. Especially, DIMC surpasses the state-of-the-art methods by 0.2048 in accuracy under extreme view missing scenarios.

In this paper, we investigate the physical layer security for multi-beam satellite communications in the presence of multiple eavesdroppers (Eves). In particular, a worst-case eavesdropping scheme is considered. To achieve a positive secrecy rate, we consider a non-orthogonal multiple access (NOMA) scheme with imperfect channel state information on Eves. Fur-thermore, we design a robust precoding algorithm to maximize the achievable secrecy rate of legitimate ground user equipments, which satisfies the quality of service for each user, secure outage probability constraint and the NOMA decoding order between legitimate users. In contrast to the conventional total transmit power constraint, the algorithm is designed under joint total and per-beam transmit power constraints. We first combine the decomposition-based large deviation inequality, the arithmetic-geometric mean inequality with a penalty function iterative algorithm to solve the non-convex precoding problem, and further analyze the computational complexity of the algorithm. Simulation results verify the robustness and superiority of the proposed algorithm.

Detection and monitoring of ships in the images captured from satellites or aerial vehicles is a pivotal task in maritime security applications. Recent advancements in aerial communication and computer vision has enabled real-time collection of such images as well as development of robust and precise models for ship detection. However, conventional machine learning (ML) based models are prone to security and privacy issues as the real-time data captured through aerial imagery may be exposed during transfer or after storage in the cloud server. Furthermore, real-time decision making is a challenging task with conventional ML models due to the latency incurred while transmitting large amount of data from maritime aerial network to the cloud. To address the privacy and latency challenges, we propose a privacy-preserving game-theory based federated learning approach for ship detection in aerial images from maritime network. FL improves privacy by allowing raw data to reside at the edges/clients, and game theory helps in optimizing the parameter updates that are sent to the centralized server. Evaluation results prove the efficacy of the proposed model with a prediction accuracy of 96.01%, 92.96% reduction in time complexity and also 8.28% reduction in communication overhead.

Recent technological advancements have considerably improved healthcare systems to provide various intelligent services, improving life quality. The Metaverse, often described as the next evolution of the Internet, helps the users interact with each other and the environment, thus offering a seamless connection between the virtual and physical worlds. Additionally, the Metaverse, by integrating emerging technologies, such as artificial intelligence (AI), cloud edge computing, Internet of Things (IoT), blockchain, and semantic communications, can potentially transform many vertical domains in general and the healthcare sector (healthcare Metaverse) in particular. The healthcare Metaverse holds huge potential to revolutionize the development of intelligent healthcare systems, thus presenting new opportunities for significant advancements in healthcare delivery, personalized healthcare experiences, medical education, collaborative research, and so on. However, various challenges are associated with the realization of the healthcare Metaverse, such as privacy, interoperability, data management, and security. Federated learning (FL), a new branch of AI, opens up enormous opportunities to deal with the aforementioned challenges in the healthcare Metaverse by exploiting the data and computing resources available at the distributed devices. This motivated us to present a survey on adopting FL for the healthcare Metaverse. Initially, we present the preliminaries of IoT-based healthcare systems, FL in conventional healthcare, and the healthcare Metaverse. Furthermore, the benefits of the FL in the healthcare Metaverse are discussed. Subsequently, we discuss the several applications of FL-enabled healthcare Metaverse, including medical diagnosis, patient monitoring, medical education, infectious disease, and drug discovery. Finally, we highlight the significant challenges and potential solutions toward realizing FL in the healthcare Metaverse.