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Decentralized Federated Graph Learning With Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku 102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users’ efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Workload Prediction of Virtual Machines Using Integrated Deep Learning Approaches Over Cloud Data Centers 1st

Workload Prediction of Virtual Machines Using Integrated Dee...

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Leka, Habte Lejebo Fengli, Zhang Kenea, Ayantu Tesfaye Sharma, Durga P. Tegene, Abebe Tamrat School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama1888 Ethiopia AMUIT MOEFDRE Under UNDP & MAISM RTU Kota Kota India

ISBN: (纸本)9789811954023

Exponential growth in the use of cloud computing services makes it difficult to forecast loads of virtual machines (VMs). Accurate virtual machine (VM) workload forecasting is the most critical task in appropriately managing cloud resources such as memory and central processing units while minimizing energy usage. To address this problem, an integrated deep learning model is proposed in this research paper. The model employs two popular neural networks: a bidirectional Long Short-Term Memory Network (BiLSTM) with a convolutional neural networks (CNN). The CNN component pulls high-level attributes from all VM workload data, whereas the BiLSTM component forecasts future VM workload. The experimental results reveal that the suggested model outperforms commonly used workload prediction methods in terms of forecasting accuracy of VMs workloads in cloud computing environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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A Study of EMG Signals Using QNET Myoelectric Board and LabVIEW software 4

A Study of EMG Signals Using QNET Myoelectric Board and LabV...

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4th International Conference on Applied Automation and Industrial Diagnostics, ICAAID 2022

作者： Alharbi, Hadeel Fahad Patel, Akshet Dasari, Uday Randhawa, Princy University of ha'Il Hail College of Computer Science and Engineering Department of Computer Science and Software Engineering 1234 Saudi Arabia Manipal University Jaipur Department of Mechatronics Engineering Jaipur India Manipal University Jaipur Department of Information Technology Engineering Jaipur India

ISBN: (纸本)9781728189390

This project's aim is to monitor the electrical activity of the user's muscle cells using the QNET Myoelectric Board and NI ELVIS II+ board. An opto-isolated sensor and a grounding strap are included in this electromyograph. The board includes an on-board signal processing and filtering mechanism, as well as a PCI-based data collection system. The board is connected using the LabVIEW application, which stands for Laboratory Virtual Instrument engineering Workbench (LabVIEW). LabVIEW is a graphical programming language and hence a direct connection between the user and the board is possible. The data is acquired, and five assessment criteria are applied to it. Graphs are shown based on time stamps for two participants (male and female), and the analyzed data is compiled to draw crucial conclusions. © 2022 IEEE.

关键词： Machine learning

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The Essence of Perceived Compatibility and Behavioral Intention in Mobile Money Use

The Essence of Perceived Compatibility and Behavioral Intent...

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2024 International Conference on Advanced Information Scientific Development, ICAISD 2024

作者： Susanti, Leni Mohamad, Siti Hajar Savitri, Mila Andria Saputra, Karen Etania Alamsyah, Doni Purnama Setiadi, Ahmad Bina Nusantara University Binus Business School Entrepreneurship Department Jakarta Indonesia Management and Science University Department of Business Management and Law Selangor Malaysia Bina Nusantara University School of Design Interior Design Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Universitas Bina Sarana Informatika Faculty of Information Technology Software Engineering Department Jakarta Indonesia

ISBN: (纸本)9798350392036

Today, financial technology is a part of consumer needs. Mobile payments are used to support daily transactions, so a study of factors that can support behavioral intention and mobile money use is conducted. This study examines the support of digital literacy, perceived security, and perceived compatibility of mobile payments in increasing behavioral intention and mobile money use. The research was conducted on mobile payment users in Bandung City (Indonesia), and 416 research participants were asked using a questionnaire. The research was conducted using a quantitative survey and was processed using the SmartPLS tool. Two tests, namely, the PLS Algorithm and Bootstrapping, were used to test the research model. Next, the research hypothesis testing was used to explain the results. The research results found positive support from digital literacy, perceived security, and perceived compatibility on mobile payment behavioral intention. However, only perceived compatibility can directly increase the use of mobile money. In line with the hypothesis test, behavioral intention can support increased mobile money use by digital payment users. The research findings explain the ability of behavioral intention to mediate support for digital literacy, perceived security, and perceived compatibility in mobile money use. © 2024 IEEE.

关键词： Mobile commerce

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Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

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computers, Materials & Continua 2023年第7期76卷 975-992页

作者： Hadeel Alsolai Mohammed Rizwanullah Mashael Maashi Mahmoud Othman Amani A.Alneil Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityP.O.box 103786Riyadh11543Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)*** presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-ASC technique,three major processes are *** the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

关键词： Soil classification earth sciences machine learning parameter optimization metaheuristics

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A comparative study of deep learning models for early diagnosis of monkeypox and chickenpox

A comparative study of deep learning models for early diagno...

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International Conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Maliha Khalid Muhammad Azam Zia Muhammad Asif Department of Computer Science University of Agriculture Faisalabad Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Lahore Pakistan

ISBN: (数字)9781837243150

Image processing enhances digital images by extracting valuable information and improving clarity through edge recognition, color modification, and filtering techniques. These methods facilitate the creation of training images that enhance the robustness and generalization of neural networks, improving diagnostic efficiency and accuracy. The similar symptoms of chickenpox and monkeypox make early clinical diagnosis difficult, necessitating rapid and precise diagnostic techniques. This study uses deep learning models to tackle the problem of correctly diagnosing chickenpox and monkeypox, facilitating early diagnosis and quick medical intervention for improving public health and epidemic management. Models such as DenseNet121, DenseNet169, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, MobileNetV2, ResNet50, ResNet101, VGG16, VGG19, and Xception have been used to classify and distinguish conditions. Advanced image processing techniques, including object detection and data normalization, improve model generalization and resilience. Among the others, MobileNet and DenseNet121 achieved highest accuracy rates which is 0.9943 and 0.9914 respectively, while MobileNet offered superior speed. The DenseNet169 model has highest precision and recall which is 0.9958 and f1-score is 0.9956 with an accuracy of 0.9857. The study also demonstrates that DL models are critical in developing robust and effective systems for detecting infectious diseases, aiding in precise and efficient healthcare solutions.

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QUIC meets ICN: A Versatile Wireless Transport Strategy in Multi-access Edge Environments

QUIC meets ICN: A Versatile Wireless Transport Strategy in M...

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International Workshop on Quality of Service

作者： Yaodong Huang Changkang Mo Tianhang Liu Biying Kong Lei Zhang Yukun Yuan Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science and Engineering University of Tennessee at Chattanooga Chattanooga TN USA

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Information-centric Networking in edge computing environments exhibits the potential to significantly enhance the efficiency, reliability, and security of data transmission, making it a promising technology for future network deployments. However, the differences from traditional networks require applications to actively redevelop and redeploy onto edge devices, incurring additional costs for the proliferation of ICN applications. In this paper, we propose a system to adapt QUIC protocol over ICN networks in multi-access edge networks. The system aims to expand the application repertoire for ICN by providing a smooth transition of applications using QUIC to run on ICN networks. We design an ICN-QUIC conversion layer to manage the transmission of data from QUIC-based applications. We implement and evaluate the designed system. The experiment results show that, compared to existing networks, our system can enhance the transmission efficiency, i.e., up to 20 times better goodput in multicast situations, and achieves comparable results in unicast scenarios. We test the scalability of our system in real edge and wireless environments. We also deploy the real applications over the proposed system to demonstrate its compatibility in ICN and MEC environments.

关键词： Wireless communication Protocols Unicast Scalability Quality of service Data communication Communication system security

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Enabling Ensemble Reinforcement Learning with ICN in Mobile Edge Computing Environments

Enabling Ensemble Reinforcement Learning with ICN in Mobile ...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Yaodong Huang Biying Kong Wenliang Feng Changkang Mo Han Liu Yukun Yuan Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science and Engineering University of Tennessee at Chattanooga Chattanooga TN USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Smart devices now own the capability to conduct machine learning tasks. Such capabilities offer the potential to improve the learning efficiency and accuracy for certain difficult learning tasks with information sharing among edge devices. In this paper, we discuss the implementation of ensemble reinforcement learning in mobile edge computing environments in real-time. We propose a novel system using Information-centric Networking to facilitate fast and efficient multicast transmission over edge devices. The system improves the efficiency and accuracy of ensemble reinforcement learning over edge devices by using targeted data transmission. To achieve efficient and instant transmission, we propose a new transport layer protocol as the middleware for upper-layer learning data and lower-layer ICN links and radios. We implement the proposed system using real edge devices and conduct experiments with several training testbeds to evaluate the performances. The results from the experiments show that the system can achieve up to 28.47% better reward within the same iterations compared to traditional multicast networks and 7.26 times reward compared to training without instant information sharing.

关键词： Training Performance evaluation Multi-access edge computing Accuracy Protocols Information sharing Reinforcement learning Smart systems Real-time systems Smart devices

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Reconfigurable Intelligent Surface Assisted Semantic Communication Systems

Reconfigurable Intelligent Surface Assisted Semantic Communi...

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2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Shi, Jiajia Chan, Tse-Tin Pan, Haoyuan Lok, Tat-Ming The Chinese University of Hong Kong Department of Information Engineering Hong Kong The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350316728

Semantic communication, which focuses on conveying the meaning of information rather than exact bit reconstruction, has gained considerable attention in recent years. Meanwhile, reconfigurable intelligent surface (RIS) is a promising technology that can achieve high spectral and energy efficiency by dynamically reflecting incident signals through programmable passive components. In this paper, we put forth a semantic communication scheme aided by RIS. Using text transmission as an example, experimental results demonstrate that the RIS-Assisted semantic communication system outperforms the point-To-point semantic communication system in terms of bilingual evaluation understudy (BLEU) scores in Rayleigh fading channels, especially at low signal-To-noise ratio (SNR) regimes. In addition, the RIS-Assisted semantic communication system exhibits superior robustness against channel estimation errors compared to its point-To-point counterpart. RIS can improve performance as it provides extra line-of-sight (LoS) paths and enhances signal propagation conditions compared to point-To-point systems. © 2023 IEEE.

关键词： Fading channels

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Domain Knowledge Graph Completion Based on Attribute Hierarchy 23

Domain Knowledge Graph Completion Based on Attribute Hierarc...

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Proceedings of the 2023 7th International Conference on Electronic Information Technology and computer engineering

作者： Ning Lan Shuqun Yang Department of Computer Science Software Engineering Institute of Guangzhou China School of Electronic and Electric Engineering Shanghai University of Engineering Science China

ISBN: (纸本)9798400708305

Knowledge graph completion is to solve the problem of lack of entities and relations in knowledge graphs. Existing Knowledge graph completion methods mainly embed entities and relations into latent vectors, and numerous researches have taken the rich relationships in knowledge graph into account such as category, entity, relation and semantic. However, only a few studies consider the relation between attributes, which is the basis of describing the entity. This paper proposes the Attribute Hierarchy Knowledge Graph Completion (AH-KGC) method, aiming at leveraging the attribute relation to find the missing obligatory property of entities. Primarily, in AH-KGC, we have discussed the attributes prerequisite relation, which can be described as a tree-like hierarchical structure, and then adopt the search algorithm of preorder traversal based on the hierarchy to find out the missing attributes. Specifically, we prove that attribute prerequisite is a special case of implication, thus can obtain attribute hierarchy from implications relation, which can easily be obtained in much mature research such as expert systems and make full use of the knowledge in various fields to make up for the vacancy of domain knowledge in the knowledge graph. The experiment has been performed on the CN-DBpedia and Probase datasets. The result demonstrates that AH-KGC can effectively complete the missing attributes of entities in knowledge graphs and achieve 100% accuracy under our evaluation system.

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