Electric scooters (ESs) serve as smart city terminals by uploading real-time charging data to improve mobility and energy efficiency. Battery swapping service has emerged as a transformative solution for energy replenishmen of ESs. Time-of-use (TOU) pricing is commonly adopted to encourage off-peak electricity usage by offering different rates. However, the rigid work schedules of ES drivers always hinder the optimization of battery swapping, leading to escalated charging costs. Existing literature fails to account for the practical constraints of battery charging in real-world scenarios. To tackle thisparadox, we studied battery swap stations in Chengdu. Our findings reveal that the cost of swapping stations could be remarkably reduced by optimizingintrinsic, energy-greedy charging power strategies rather than by altering theextrinsic, rigid swapping schedules. Further analysis indicates 63% of charged batteries remain idle, presenting an opportunity to optimize charging power for cost savings without disrupting swapping schedules. Motivated by these insights, we propose PowerRL, anewadaptive charging power strategy empowered by spatio-temporal reinforcement learning. Specifically, we employ a spatio-temporal gated recurrent unit to predict the demand for battery swapping, which is then used to optimize charging power through reinforcement learning. Applied to the large-scale dataset, PowerRL could reduce per-unit electricity costs by 25.7% while still accommodating driver swapping schedules, thereby harmonizing TOU pricing with the rigidity of ES swapping service.

Federated learning (FL) is a promising paradigm of distributed machine learning, since it enables the collaborative training of machine learning models across multiple edge devices without compromising privacy. However, when deploying the FL framework in real-world scenarios, the global model is plagued by the aggregate loss issue, which can result in inefficient and unfair model training. Existing works in the literature either fail to ensure high efficiency in model updates or can not achieve fair model updates. Inspired by the art of “Less is more”, we propose FedBFO, a Federated learning framework with Bi-level First-order Optimization. The key idea of FedBFO is to treat model aggregation as the second optimization of local models, which utilize knowledge trimming to design a more tailored global model for each device. To this end, we first propose a knowledgeaware trimming scheme to adaptively aggregate tailored global models for local devices. We then present a anomaly-aware trimming mechanism to iteratively select qualified local models. The theoretical analysis shows that FedBFO can ensure the efficiency, fairness, and convergence of models. Experimental results demonstrate that FedBFO outperforms six state-of-the-art baselines with a 6.7x convergence speed than FedAvg, achieving better tradeoff convergence performance, efficiency, and fairness.

Based on emerging artificial intelligence (AI) tasks, cloud-edge–terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats, such as malicious network reconnaissance, side-channel attacks, and distributed denial of service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of moving target defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this article, we first provide a thorough analysis of the threats in the cloud-edge–terminal network. Then, we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this article is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network.

Federated learning (FL) is a promising artificial intelligence framework that enables clients to collectively train models with data privacy. However, in real-world scenarios, to construct practical FL frameworks, several challenges have to be addressed, including statistical heterogeneity, constrained resources, and fairness. Therefore, we first investigate anaggregation gapcaused by statistical heterogeneity during local model initialization, which not only causes additional computational overhead for clients but also leads to the degradation of fairness. To bridge this gap, we proposepFedCal, a novelpersonalizedfederated learning with lightweight adaptivecalibration strategy that performs calibration compensation through the prior knowledge of clients. Specifically, we introduce compensation for each client at the model initialization, with the compensation derived from the global gradient and the latest gradient bias. To enhance the calibration effect, we introduce a smoothing-based calibration strategy, and we design an adaptive calibration strategy. A representative example demonstrates that the proposed calibration and smoothing strategies improve fairness for clients. The theoretical analysis indicates that with an appropriate learning rate, pFedCal converges to a first-order stationary point for non-convex loss functions. Comprehensive experimental results show that pFedCal achieves faster convergence, higher accuracy, and improved fairness than the state-of-the-art methods.

Federated learning has emerged as a promising approach for fault diagnosis, as its ability to learn from decentralized data while preserving client privacy for industry. Yet, it also brings the problem of nonidentically and independently distributed (Non-IID) data, which can result in model convergence delay and performance degradation. Recent research aims to alleviate the problem caused by cross-domain without considering by cross-location. However, it is common in industrial production to have devices across different monitoring locations. Furthermore, experimental results indicate that the diagnostic models' performance of the latest techniques is significantly affected. To address the cross-location Non-IID data problem, we propose DecFFD, a personalized federated fault diagnosis framework that decouples global and personalized features. In DecFFD, we design a reconstructor for each client that acts as a supervisor and decoupler to disentangle global and personalized features. We then present a client alignment algorithm to eliminate the differences in global features among clients. In addition, we provide a theoretical analysis of fairness and generalization capability, offering a theoretical guarantee for model convergence. Finally, extensive experiments are conducted on two real-world datasets. Experimental results show that the accuracy of DecFFD outperforms the accuracy that of the state-of-the-art approach by 14.67% and converges at a faster rate.

Federated learning (FL) opens a new promising paradigm for the Industrial Internet of Things (IoT) since it can collaboratively train machine learning models without sharing private data. However, deploying FL frameworks in real IoT scenarios faces three critical challenges, i.e., statistical heterogeneity, resource constraint, and fairness. To address these challenges, we design a fair and efficient FL method, termed FedASA, which can address the challenge of statistical heterogeneity in resource-constrained scenarios by determining the shared architecture adaptively. In FedASA, we first present a cell-wised shared architecture selection strategy, which can adaptively construct the shared architecture for each device. We then design a cell-based aggregation algorithm for aggregating heterogeneous shared architectures. In addition, we provide a theoretical analysis of the federated error bound, which provides a theoretical guarantee for the fairness. At the same time, we prove the convergence of FedASA at the first-order stationary point. We evaluate the performance of FedASA through extensive simulation and experiments. Experimental results in cross-location scenarios show that FedASA outperformed the state-of-the-art approaches, improving accuracy by up to 13.27% with better fairness and faster convergence and communication requirement has been reduced by 81.49%.