Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server communication since the end devices typically have very limited communication bandwidth. This article presents an enhanced federated learning technique by proposing an asynchronous learning strategy on the clients and a temporally weighted aggregation of the local models on the server. In the asynchronous learning strategy, different layers of the deep neural networks (DNNs) are categorized into shallow and deep layers, and the parameters of the deep layers are updated less frequently than those of the shallow layers. Furthermore, a temporally weighted aggregation strategy is introduced on the server to make use of the previously trained local models, thereby enhancing the accuracy and convergence of the central model. The proposed algorithm is empirically on two data sets with different DNNs. Our results demonstrate that the proposed asynchronous federated deep learning outperforms the baseline algorithm both in terms of communication cost and model accuracy.

With the advancement of communication technologies and the development of the smart grid, today's physical power systems present an ever-growing dependency on cyber resources. It increases cyber vulnerabilities, causing safety and system stability concerns. In this article, a data-driven resilient automatic generation control (AGC) scheme is proposed under a false data injection attack (FDIA). The key idea is to identify the relationship between AGC signals and system operational conditions. This is achieved by the proposed regression-based FDIA signal predictions, including sequence-to-point prediction and the long short-term memory network-based prediction. It allows us to reconstruct the AGC control signals without being affected by FDIAs and to attenuate attacks in the closed control loop, thus alleviating the impact of FDIA on system performance. Numerical results carried out on the benchmark systems validate the effectiveness of the proposed method.

Power system state estimation (PSSE) is the foundation of energy management system applications. Hence, operators impose stringent requirements on PSSE data integrity. False data injection attacks (FDIAs) can cause risks to PSSE data-driven operations and demand mitigation. In this article, we present a two-step FDIA detector design. In step one, we study a novel stealthy attack policy by simultaneously considering the attacker’s cost reduction and damage production. In step two, with the aid of a deep autoencoding Gaussian mixture model (DAGMM), we design an unsupervised detection scheme to detect the stealthy attack. The DAGMM-based detector can meet the requirement of rapidity, unsupervisedness, and data imbalance tolerance. Eventually, we simulate and validate the stealthy attack policy and the corresponding detector using the benchmark IEEE 39-bus and 118-bus systems.

This article discusses the different forms of powder bed fusion (PBF) techniques, namely laser powder bed fusion (LPBF), electron beam powder bed fusion (EB-PBF) and large-area pulsed laser powder bed fusion (L-APBF). The challenges faced in multimetal additive manufacturing, including material compatibility, porosity, cracks, loss of alloying elements and oxide inclusions, have been extensively discussed. Solutions proposed to overcome these challenges include the optimization of printing parameters, the use of support structures, and post-processing techniques. Future research on metal composites, functionally graded materials, multi-alloy structures and materials with tailored properties are needed to address these challenges and improve the quality and reliability of the final product. The advancement of multimetal additive manufacturing can offer significant benefits for various industries.

Interactive evolutionary algorithms have been applied to personalized search, in which less user fatigue and efficient search are pursued. Motivated by this, we present a fast interactive estimation of distribution algorithm (IEDA) by using the domain knowledge of personalized search. We first induce a Bayesian model to describe the distribution of the new user's preference on the variables from the social knowledge of personalized search. Then we employ the model to enhance the performance of IEDA in two aspects, that is: 1) dramatically reducing the initial huge space to a preferred subspace and 2) generating the individuals of estimation of distribution algorithm(EDA) by using it as a probabilistic model. The Bayesian model is updated along with the implementation of the EDA. To effectively evaluate individuals, we further present a method to quantitatively express the preference of the user based on the human-computer interactions and train a radial basis function neural network as the fitness surrogate. The proposed algorithm is applied to a laptop search, and its superiorities in alleviating user fatigue and speeding up the search procedure are empirically demonstrated.