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This paper presents two tandemly stacked forward and inverse deep neural networks to model and optimize cylindrical shaped transmissive meta-atoms for UV–Vis regime. Conventional modeling of these subwavelength meta-atoms calls for repetitive solution of Maxwell equations over each instance of a mesh grid using some high-end commercial EM simulator. In contrast, Deep Learning (DL)-based approaches can significantly expedite this process owing to their abilities to intelligently learn and approximate Maxwell's equations without explicitly solving them, hence anticipating the electromagnetic (EM) response of a given meta-atom within a split-second. Despite of the advantages of DL-based solutions for metasurface design and optimization, dataset collection for training DL models still requires time-tedious conventional approaches. Here, we aim to enhance the learning performance while reducing the dataset requirements and the complexity of the proposed forward and inverse networks by enriching them with more underlying physical facts and substantial knowledge of a particular problem. Therefore, in addition to the list of geometrical parameters of the cylindrical meta-atoms, the proposed forward neural network is also fed with some additional material and physics related parameters as a part of its input, including EM spectral information, material type (dielectric or plasmon), maximum height (based on the fabrication constraints), and period of the cylindrical rod. This results in enhancing the model's performance while utilizing minimum training samples, and predicting the EM transmission amplitude and phase with negligible error tolerance, i.e. MSE: 2.1 × 10−3, for a given dielectric or plasmonic meta-atom at various operating wavelengths (within the targeted regimes). Optimization of such meta-structures is another challenging task that requires hit and trial intuitive guesses, lengthy parametric sweeps and time taking conventional intelligent algorithms. Therefore, an inverse design neural network is stacked behind the trained forward modeling network and their tandem assembly is trained together to predict the best set of dimensions and the most suitable material to achieve the desired response. The inverse design network is also fed with the same additional information including all the aforementioned parameters, as the forward mapping network. In order to assess the impact of incorporating this supplementary problem specific information, a comparative study regarding the number of hidden layers and the amount of training dataset size is carried out for the forward and tandem inverse neural networks. This comparative analysis shows that the loss is greatly decreased, and the networks learns efficiently using the proposed scheme with more knowledge about the problem under consideration. As a result, we show that even with a smaller training dataset and lesser number of hidden layers, the model can realize an appropriate MSE.

In recent years, the way that machine learning is used has undergone a paradigm shift driven by distributed and collaborative learning. Several approaches have emerged to enable pervasive computing and distributed learning in ubiquitous Internet of Things (IoT) systems. Numerous decentralized strategies have been proposed to deal with the limitations of centralized learning, including privacy and latency due to sharing local data, while utilizing distributed computations as a promising substitute to centralized learning. However, such distributed learning schemes come with new security and privacy concerns that should be addressed. Thus, in this paper, we first provide an overview for the emerging paradigms developed for distributed learning. Then, we performed a comprehensive survey for the privacy and security challenges associated with distributed learning along with the presented solutions to overcome them. Furthermore, we highlight key challenges and open future research directions toward implementing more robust distributed systems.

In order to maintain a satisfactory performance in the midst of rapid growth of mobile traffic, the mobile network infrastructure needs to be scaled. Thus there has been significant interest in scalability of mobile core networks and a variety of scaling solutions have been proposed that rely on horizontal scaling or vertical scaling. These solutions handle the scaling of the mobile core networks’ elements on virtual machines (which normally take at while to create) with the help of customized modules at the cost of increased overheads. Utilizing Amazon Web Services (AWS) embedded features, we present two predictive horizontal auto-scalers for containerized and non-containerized versions of EPC that scales the two versions of the EPC according to their respective CPU utilization. Additionally, we propose an efficient task assignment scheme for AWS that aims to maximize throughput and achieve fairness among competing instances. In particular, we propose two solutions: Relaxed Optimized Solution (ROS) and a Heuristic Approach (HA). Leveraging AWS environment, we implemented and evaluated the two proposed auto-scaling models based on the attachment success rate, latency, CPU usage and RAM usage. Our findings show the superiority of container-based model over VM-based model in terms of resource utilization. The obtained results for the two proposed task assignment solutions demonstrates a significant improvement both in fairness and throughput compared to other existing solutions.

Due to the increasing number of chronic disease patients, continuous health monitoring has become the top priority for health-care providers and has posed a major stimulus for the development of scalable and energy efficient mobile health systems. Collected data in such systems are highly critical and can be affected by wireless network conditions, which in return, motivates the need for a preprocessing stage that optimizes data delivery in an adaptive manner with respect to network dynamics. We present in this paper adaptive single and multiple modality data compression schemes based on deep learning approach, which consider acquired data characteristics and network dynamics for providing energy efficient data delivery. Results indicate that: 1) the proposed adaptive single modality compression scheme outperforms conventional compression methods by 13.24% and 43.75% reductions in distortion and processing time, respectively; 2) the proposed adaptive multiple modality compression further decreases the distortion by 3.71% and 72.37% when compared with the proposed single modality scheme and conventional methods through leveraging inter-modality correlations; and 3) adaptive multiple modality compression demonstrates its efficiency in terms of energy consumption, computational complexity, and responding to different network states. Hence, our approach is suitable for mobile health applications (mHealth), where the smart preprocessing of vital signs can enhance energy consumption, reduce storage, and cut down transmission delays to the mHealth cloud.