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Energy-efficient and Privacy-preserving Edge Intelligence for 6G-Empowered Intelligent Transportation Systems

IEEE Network 2025年

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Xiao, Ziheng Guo, Tingting Liu, Shenghao Sun, Jiaqi Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan Kedao Geographic Information Engineering Co. Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of

6G-empowered Intelligent Transportation Systems (ITS) generate large amounts of data through millions of devices and sensors at the terminal and network edge. Edge intelligence advances the frontier of data-driven artificial intelligence (AI) to the network edge, which enables AI applications to better utilize edge data. Broader connectivity, higher device density, and stronger data exchange present challenges for reliable device communication and privacy data protection in 6G-empowered ITS. To address these challenges, this paper proposes an energy-efficient and privacy-preserving edge intelligence framework named EP-EI. EP-EI deploys a deep reinforcement learning-based cluster head selection model at the edge to ensure energy-efficient communication for terminal devices. Meanwhile, EP-EI designs a privacy-aware hierarchical computing framework for terminal-edge-cloud to prevent privacy leakage. Furthermore, the application scenarios, challenges, and future directions of EP-EI are discussed. Finally, simulation experiments in a specific intelligent transportation case study demonstrate that EP-EI can provide energy-efficient and privacy-protecting services. © 2025 IEEE.

关键词： Differential privacy

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Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Learning Approach 12

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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data Poisoning Attack to X-armed Bandits 21

Data Poisoning Attack to X-armed Bandits

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21st IEEE International Conference on Trust, security and Privacy in Computing and Communications, TrustCom 2022

作者： Luo, Zhi Li, Youqi Chen, Lixing Xu, Zichuan Zhou, Pan Huazhong University of Science and Technology School of Cyber Science and Engineering China Beijing Institute of Technology School of Cyberspace Science and Technology School of Computer Science China Shanghai Jiao Tong University Institute of Cyber Science and Technology China Dalian University of Technology School of Software China Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China

ISBN: (纸本)9781665494250

X-armed bandits have achieved the state-of-the-art performance in optimizing unknown stochastic continuous functions, which can model many machine learning tasks, specially in big data-driven personalized recommendation. However, bandit algorithms are vulnerable to adversarial attacks. Existing works mainly focus on attacking multi-armed bandits in discrete setting;nevertheless, the attacks against X-armed bandits in continuous setting have not been well explored. In this paper, we aim to bridge this gap and investigate the robustness problem for the X-armed bandits. Specifically, we consider data poisoning attack and propose an attack algorithm named Confidence Poisoning Attack algorithm, which could hijack the clean tree-based X-armed bandits algorithm, i.e., high confidence tree (HCT) and make it choose the nodes including the arm targeted by the attacker very frequently with a sub-linear attack cost, i.e., O(Tα)(0 © 2022 IEEE.

关键词： Stochastic systems

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A Digital Twin-based Framework of Motion Control and State Monitoring for Pneumatic Muscle

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Recent Patents on Mechanical engineering 2023年第3期16卷 203-213页

作者： Xie, Shenglong Liu, Wenyuan Duan, Huiru Chen, Dijian Wan, Yanjian Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Ji-liang University Zhejiang Hangzhou310018 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma'anshan243032 China

Introduction: The current digital twin systems usually have the drawback of high cost and complex technology, and it is necessary to develop a simple solution to reduce the cost and cycle for the development of digital twin systems, especially for small projects or systems with simple structures. Objective: A low-cost patent technology of digital twin system was proposed by taking the motion control and state monitoring system (MCSMS) of pneumatic muscle as an example. Methods: The MCSMS is developed based on the browser/server architecture. The software of 3ds Max and SolidWorks are used to make the virtual model, *** and JavaScript are applied to build the browser side. data of the physical world is collected and processed on the server side firstly, and then is sent to the browser side through HTTP communication protocol to realize data interchange between the browser and server. Results: In the roaming experiment and the experiment of motion control and state monitoring of pneumatic muscle, the MCSMS can work smoothly without obvious delay and has good real-time performance, which can realize the 3D visual monitoring of the pneumatic muscle very well. Conclusion: The experimental results indicate that the proposed method possesses the ability of good feasibility and effectiveness. © 2023 Bentham science Publishers.

关键词： Muscle

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M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Network for Segmenting Pathology Lesion of the Focal Lymphocytic Sialadenitis 1

M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Ne...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Han, Hao Lu, Feng Deng, Yanhan Luo, Xiaofang Jin, Hai Tu, Wei Xie, Xia School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan430074 China Tongji Medical College Huazhong University of Science and Technology Tongji Hospital Wuhan430022 China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9798350358780

In the medical realm, the pivotal role of pathological Whole Slide Images (WSIs) in detecting cancer, tracking disease progression, and evaluating treatment efficacy is indisputable. Nevertheless, the identification and quantification of lesion areas in these gigapixel WSIs present a significant challenge due to their substantial size and the intricate details of lesions. To address these issues, we developed a novel multi-resolution and multi-scale cross fusion network (M2CF-Net), adept at managing large-scale pathological WSIs and capturing both fine details and context. Our model particularly focuses on segmenting local lymphocyte infiltration lesions in pathological WSIs of patients diagnosed with primary Sjogren's syndrome. By employing a patch-based training approach and combining interconnected elements via a multi-scale fusion technique, we enhance our model's capacity to detect and analyze structures and features in minor salivary gland section WSIs. Extensive experiments and ablation studies conducted on real-world clinical datasets affirm our model's superior accuracy in identifying lymphocyte-infiltrated regions over state-of-the-art models, with a performance improvement of up to 4.32% in terms of the Dice Similarity Coefficient. © 2023 IEEE.

关键词： Pathology

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A High-capacity Information Hiding Scheme Based on Blakley Secret Sharing 4

A High-capacity Information Hiding Scheme Based on Blakley S...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Zhou, Jincheng Li, Yue Shen, Yujie Tang, Dan Cai, Hongliang Gong, Jing Bai, Ningchao School of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Province Engineering Technology Research Center of Support Software of lnformatization Application Chengdu China School of Computer Science and Engineering Sichuan University of Science & Engineering Yibin China Sichuan Provincial Big Data Technical Service Center Chengdu China

ISBN: (纸本)9798331541729

A high-capacity reversible information hiding scheme based on Blakley secret sharing is proposed to address the issues of low embedding rate and weak disaster recovery performance of images when using reversible information hiding to protect data in scenarios with large data transmission. The scheme employs Euclidean division and a two-dimensional modulus function to establish a robust mapping relationship between the secret information and the pixels of the cover image. Subsequently, the values derived from the preprocessing of the secret information are incorporated into the secret sharing method proposed by Blakley as the elements of the secret vector. The proposed scheme exploits the disaster-tolerance property of the secret sharing method and enhances the redundancy of the secret-carrying encrypted image, thereby improving the embedding rate and security of the algorithm while maintaining the security of the secret sharing. © 2024 IEEE.

关键词： Euclidean division Information hiding Modulus function Secret sharing

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FDGLib: A Communication Library for Efficient Large-Scale Graph Processing in FPGA-Accelerated data centers

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Journal of Computer science & technology 2021年第5期36卷 1051-1070页

作者： Yu-Wei Wu Qing-Gang Wang Long Zheng Xiao-Fei Liao Hai Jin Wen-Bin Jiang Ran Zheng Kan Hu National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

With the rapid growth of real-world graphs,the size of which can easily exceed the on-chip(board)storage capacity of an accelerator,processing large-scale graphs on a single Field Programmable Gate Array(FPGA)becomes *** multi-FPGA acceleration is of great necessity and *** cloud providers(e.g.,Amazon,Microsoft,and Baidu)now expose FPGAs to users in their data centers,providing opportunities to accelerate large-scale graph *** this paper,we present a communication library,called FDGLib,which can easily scale out any existing single FPGA-based graph accelerator to a distributed version in a data center,with minimal hardware engineering *** provides six APIs that can be easily used and integrated into any FPGA-based graph accelerator with only a few lines of code *** the torus-based FPGA interconnection in data centers,FDGLib also improves communication efficiency using simple yet effective torus-friendly graph partition and placement *** interface FDGLib into AccuGraph,a state-of-the-art graph *** results on a 32-node Microsoft Catapult-like data center show that the distributed AccuGraph can be 2.32x and 4.77x faster than a state-of-the-art distributed FPGA-based graph accelerator ForeGraph and a distributed CPU-based graph system Gemini,with better scalability.

关键词： data center accelerator graph processing distributed architecture communication optimization

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SADBA: Self-Adaptive Distributed Backdoor Attack Against Federated Learning 39

SADBA: Self-Adaptive Distributed Backdoor Attack Against Fed...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Feng, Jun Lai, Yuzhe Sun, Hong Ren, Bocheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Economics Wuhan Textile University China School of Computer Science and Technology Hainan University China

ISBN: (纸本)157735897X

Backdoor attacks in federated learning (FL) face challenges such as lower attack success rates and compromised main task accuracy (MA) compared to local training. Existing methods like distributed backdoor attack (DBA) mitigate these issues by modifying malicious clients’ updates and partitioning global triggers to enhance backdoor persistence and stealth. The recent full combination backdoor attack (FCBA) further improves backdoor efficiency with a full combination strategy. However, these methods are mainly applicable in small-scale FL. In large-scale FL, small trigger patterns weaken impact, and scaling them requires controlling exponentially more clients, which poses significant challenges, while simply reverting to DBA may decrease backdoor performance. To overcome these challenges, we propose the self-adaptive distributed backdoor attack (SADBA), which achieves similar performance to FCBA with a lower percentage of malicious clients (PMC). It also adapts more flexibly through an optimized model poisoning strategy and a self-adaptive data poisoning strategy. Experiments demonstrate SADBA outperforms state-of-the-art methods, achieving higher or comparable backdoor performance and MA across various datasets with limited PMC. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment 21

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Huang, Shuai Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350329223

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models. © 2023 IEEE.

关键词： Alignment

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AFaVS: Accurate Yet Fast Version Switching for Graph Processing Systems 39

AFaVS: Accurate Yet Fast Version Switching for Graph Process...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Zheng, Long Ye, Xiangyu Liu, Haifeng Wang, Qinggang Huang, Yu Gui, Chuangyi Yao, Pengcheng Liao, Xiaofei Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Laboratory Wuhan430074 China Zhejiang Lab Hangzhou311121 China Unsw School of Computer Science and Engineering Sydney Australia

ISBN: (纸本)9798350322279

Multi-version graph processing has been widely used to solve many real-world problems. The process of the multi-version graph processing typically includes: (1) a history graph version switching at a specific time and (2) graph processing on this history graph. Existing multi-version graph systems assume ideally that every request for a particular graph version at a particular time will have a corresponding snapshot available. However, in most cases, this is not true. Then existing solutions usually have to settle with an "approximating"version as a substitute, leading to unexpected results for the underlying graph algorithm and thus reducing the practicality of a multi-version graph system for many application scenarios *** this paper, we observe that only a few graph updates have a great impact on the final results. We therefore present AFaVS, a novel multi-version graph system that can improve accuracy effectively in both time- and memory-efficient manners. The cornerstone of AFaVS lies in a novel concept "value"that characterizes the importance of graph updates. AFaVS proposes differential management of updates based on their values and achieves higher accuracy while preserving processing and memory efficiency. AFaVS is also equipped with value-guided version switching and locality-aware optimizations to boost its overall efficiency. Our results on a variety of real-world datasets show that AFaVS outperforms four state-of-the-art multi-version graph systems by 74.35%~95.72% in terms of accuracy improvement and 57.03%~90.44% in terms of memory reduction while introducing less than 2.96% extra computing time. We have deployed AFaVS in a disaster recovery system on the production cluster of Alibaba, achieving 78.8%~90.1% fewer error rates than advanced systems at a comparable efficiency. © 2023 IEEE.

关键词： Efficiency

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