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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

IEEE Transactions on Mobile computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge computing Environment

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Bo Han Yuheng Jia Hui Liu Junhui Hou School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Ministry of Education Southeast University Nanjing China School of Computing Information Sciences Saint Francis University Tseung Kwan O Hong Kong Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong

Spectral variations pose a common challenge in analyzing hyperspectral images (HSI). To address this, low-rank tensor representation has emerged as a robust strategy, leveraging inherent correlations within HSI data. However, the spatial distribution of ground objects in HSIs is inherently irregular, existing naturally in tensor format, with numerous class-specific regions manifesting as irregular tensors. Current low-rank representation techniques are designed for regular tensor structures and overlook this fundamental irregularity in real-world HSIs, leading to performance limitations. To tackle this issue, we propose a novel model for irregular tensor low-rank representation tailored to efficiently model irregular 3D cubes. By incorporating a non-convex nuclear norm to promote low-rankness and integrating a global negative low-rank term to enhance the discriminative ability, our proposed model is formulated as a constrained optimization problem and solved using an alternating augmented Lagrangian method. Experimental validation conducted on four public datasets demonstrates the superior performance of our method compared to existing state-of-the-art approaches. The code is publicly available at https://***/hb-studying/ITLRR.

关键词： Tensors Three-dimensional displays Matrix decomposition Hyperspectral imaging Sparse matrices Indexes Solid modeling Fast Fourier transforms Correlation Optimization

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Questionable Fairness in Federated Learning

Questionable Fairness in Federated Learning

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Conference on Data science and Machine Learning Applications (CDMA)

作者： Raed Alharbi Truc Nguyen Nooshin Yousefzadeh Ahmed Aljohani College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Computatioual Science Center National Renewable Energy Laboratory Golden CO USA Computer and Information Science and Engineering Department University of Florida Gainesville FL USA Computer Science and Engineering University of North Texas Denton USA

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. This paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. The experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning.

关键词： Training Deep learning Data privacy Federated learning Prevention and mitigation Distributed databases Data science Data models Servers Multiaccess communication

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SEDNet: Synergistic Learning Network with Embedded Encoder and Dense Atrous Convolution for Vehicle Re-identification

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Alexandria engineering Journal 2025年 128卷 297-305页

作者： Mingfu Xiong Tanghao Gui Zhihong Sun Saeed Anwar Aziz Alotaibi Khan Muhammad School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan 430200 China Department of Information Security Naval University of Engineering Wuhan 430030 China The University of Western Australia (UWA) Crawley 6009 Australia Department of Computer Science College of Computing and Information Technology Taif University Taif 21974 Saudi Arabia Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab) Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea

To address the issue of information redundancy (such as color and vehicle model) caused by excessive emphasis on local features in vehicle re-identification, this paper proposes a Synergistic Learning Network with Embedded Encoder and Dense Atrous Convolution (SEDNet). The proposed SEDNet framework consists of three unique branches: a global embedded multi-head encoder (GEME), local dual-dense atrous convolution (LDAC), and auxiliary attribute embedding (AAM). The GEME branch integrates the global appearance features of the vehicle to enhance consistency in descriptions from different perspectives. To suppress redundant information such as color and vehicle model information, and refine local features, the LDAC branch employs an attention mechanism to capture multiscale features using convolutional kernels with varying dilation rates. In addition, the AAM branch uses vehicle metadata, such as direction and camera perspectives, to enhance feature robustness. Our proposed SEDNet method has been rigorously tested on the mainstream benchmark vehicle re-identification datasets, including VeRi-776, VehicleID, and VeRi-Wild. The results show that our method enhances the mAP by 2.2%, 2.2%, and 0.2%, respectively, when compared to the latest methods, all evaluated on a regular scale. Additional experiments conducted on the Market-1501 and DukeMTMC-reID datasets further verify our method’s generalization capability.

关键词： Vehicle re-identification Embedded encoder Dense atrous convolution Intelligent networks Synergistic learning

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Backdoor-Empowered Regulable Privilege Authorization for Edge-Level Graph Learning in 6G Vehicular Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Yang, Xiao Li, Gaolei Wu, Jun Zhou, Kai Li, Jianhua Yang, Wu Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China The Hong Kong Polytechnic University Department of Computing Hong Kong Harbin Engineering University College of Computer Science and Technology Harbin China

Edge-Level Graph Learning System (EGLS) exhibits diverse applicability in flow prediction, route planning, and accident forecasting. Existing EGLS studies extremely stress absolute fairness and impartiality for all users, resultantly depriving its flexibility and humanity in addressing special circumstances and rendering the privilege authorization for exceptional responses challenging to achieve. To address this limitation, we present BackPriv, the first privilege authorization methodology enabling regulable privilege assignment or revocation in EGLS. Specifically, it is achieved through a creative edge backdoor mechanism, wherein the edge training samples are poisoned via user-specific privilege tokens to induce privilege outputs from the well-trained EGLS for authorized users. Moreover, related EGLS regulating strategies were proposed to dynamically realize the addition and revocation of user privileges by system retraining to guarantee privilege control in EGLS. Additionally, a Graph Mutual information-based adaptive privilege token generation method is introduced to reduce the impact of privilege authorization on normal inputs and augment authorization functionality. Based on experiments with multiple real-world datasets, BackPriv demonstrates high success rates of privilege authorization under various scenarios ( 100%) and exerts minimal impact on normal task performance for regular inputs (mean accuracy drop 0.32%). © 1975-2011 IEEE.

关键词： Adversarial machine learning

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Xuzhi Feng, Wei Kong, Lingdong Wan, Liang Tianjin Normal University College of Computer and Information Engineering Tianjin300380 China Tianjin Key Laboratory of Student Mental Health and Intelligence Assessment China Tianjin University College of Intelligence and Computing Tianjin300350 China National University of Singapore Department of Computer Science 117597 Singapore

LiDAR semantic segmentation plays a vital role in autonomous driving. Existing voxel-based methods for LiDAR semantic segmentation apply uniform partition to the 3D Li- DAR point cloud to form a structured representation based on cartesian/cylindrical coordinates. Although these methods show impressive performance, the drawback of existing voxel-based methods remains in two aspects: (1) it requires a large enough input voxel resolution, which brings a large amount of computation cost and memory consumption. (2) it does not well handle the unbalanced point distribution of LiDAR point cloud. In this paper, we propose a non-uniform cylindrical partition network named NUC-Net to tackle the above challenges. Specifically, we propose the Arithmetic Progression of Interval (API) method to non-uniformly partition the radial axis and generate the voxel representation which is representative and efficient. Moreover, we propose a non-uniform multi-scale aggregation method to improve contextual information. Our method achieves state-of-the-art performance on SemanticKITTI and nuScenes datasets with much faster speed and much less training time. And our method can be a general component for LiDAR semantic segmentation, which significantly improves both the accuracy and efficiency of the uniform counterpart by 4× training faster and 2× GPU memory reduction and 3× inference speedup. We further provide theoretical analysis towards understanding why NUC is effective and how point distribution affects performance. Code is available at https://***/alanWXZ/NUC-Net. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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From Head to Tail: Efficient Black-box Model Inversion Attack via Long-tailed Learning

arXiv

引用

arXiv 2025年

作者： Li, Ziang Zhang, Hongguang Wang, Juan Chen, Meihui Hu, Hongxin Yi, Wenzhe Xu, Xiaoyang Yang, Mengda Ma, Chenjun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Shanghai Innovation Institute China Ant Group China Department of Computer Science and Engineering University at Buffalo SUNY United States

Model Inversion Attacks (MIAs) aim to reconstruct private training data from models, leading to privacy leakage, particularly in facial recognition systems. Although many studies have enhanced the effectiveness of white-box MIAs, less attention has been paid to improving efficiency and utility under limited attacker capabilities. Existing black-box MIAs necessitate an impractical number of queries, incurring significant overhead. Therefore, we analyze the limitations of existing MIAs and introduce Surrogate Model-based Inversion with Long-tailed Enhancement (SMILE), a high-resolution oriented and query-efficient MIA for the black-box setting. We begin by analyzing the initialization of MIAs from a data distribution perspective and propose a long-tailed surrogate training method to obtain high-quality initial points. We then enhance the attack’s effectiveness by employing the gradient-free black-box optimization algorithm selected by NGOpt. Our experiments show that SMILE outperforms existing state-of-the-art black-box MIAs while requiring only about 5% of the query overhead. Our code is available at https://***/L1ziang/SMILE. Copyright © 2025, The Authors. All rights reserved.

关键词： Face recognition

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