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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 Center for Advanced Intelligence Project RIKEN 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-ku102-8160 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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Private Computation of Boolean Functions Using Single Qubits 15th

Private Computation of Boolean Functions Using Single Qubit...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Rahmani, Zeinab Pinto, Armando N. Barbosa, Luis S. Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal Instituto de Telecomunicações Aveiro Portugal International Iberian Nanotechnology Laboratory Braga Portugal Department of Computer Science University of Minho Braga Portugal Institute of Systems and Computer Engineering Technology and Science Braga Portugal

ISBN: (纸本)9783031856990

Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. This paper introduces a two-party quantum SMC protocol designed for evaluating binary Boolean functions using single qubits. Complexity analyses demonstrate a reduction of 66.7% in required quantum resources, achieved by utilizing single qubits instead of multi-particle entangled states. However, the quantum communication cost has increased by 40% due to the amplified exchange of qubits among participants. Furthermore, we bolster security by performing additional quantum operations along the y-axis of the Bloch sphere, effectively hiding the output from potential adversaries. We design the corresponding quantum circuit and implement the proposed protocol on the IBM Qiskit platform, yielding reliable outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Qubits

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Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics

Face Presentation Attack Detection by Excavating Causal Clue...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Meiling Fang Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Recent face presentation attack detection (PAD) leverages domain adaptation (DA) and domain generalization (DG) techniques to address performance degradation on unknown domains. However, DA-based PAD methods require access to unlabeled target data, while most DG-based PAD solutions rely on a priori, i.e., known domain labels. Moreover, most DA-/DG-based methods are computationally intensive, demanding complex model architectures and/or multi-stage training processes. This paper proposes to model face PAD as a compound DG task from a causal perspective, linking it to model optimization. We excavate the causal factors hidden in the high-level representation via counterfactual intervention. Moreover, we introduce a class-guided MixStyle to enrich feature-level data distribution within classes instead of focusing on domain information. Both class-guided MixStyle and counterfactual intervention components introduce no extra trainable parameters and negligible computational resources. Extensive cross-dataset and analytic experiments demonstrate the effectiveness and efficiency of our method compared to state-of-the-art PADs. The implementation and the trained weights are publicly available 1 .

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Contextual active model selection 24

Contextual active model selection

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xuefeng Liu Fangfang Xia Rick L. Stevens Yuxin Chen Department of Computer Science University of Chicago Argonne National Laboratory Department of Computer Science University of Chicago and Argonne National Laboratory

ISBN: (纸本)9798331314385

While training models and labeling data are resource-intensive, a wealth of pre-trained models and unlabeled data exists. To effectively utilize these resources, we present an approach to actively select pre-trained models while minimizing labeling costs. We frame this as an online contextual active model selection problem: At each round, the learner receives an unlabeled data point as a context. The objective is to adaptively select the best model to make a prediction while limiting label requests. To tackle this problem, we propose CAMS, a contextual active model selection algorithm that relies on two novel components: (1) a contextual model selection mechanism, which leverages context information to make informed decisions about which model is likely to perform best for a given context, and (2) an active query component, which strategically chooses when to request labels for data points, minimizing the overall labeling cost. We provide rigorous theoretical analysis for the regret and query complexity under both adversarial and stochastic settings. Furthermore, we demonstrate the effectiveness of our algorithm on a diverse collection of benchmark classification tasks. Notably, CAMS requires substantially less labeling effort (less than 10%) compared to existing methods on CIFAR10 and DRIFT benchmarks, while achieving similar or better accuracy.

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Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN

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computers, Materials & Continua 2023年第7期76卷 175-194页

作者： Huaxin Zhou Ziying Fang Yilin Wang Mengjun Tong School of Mathematics and Computer Science Zhejiang A&F UniversityHangzhou311300China Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research Hangzhou311300China School of Humanities and Law Zhejiang A&F UniversityHangzhou311300China Department of Computer Science and Engineering Lehigh UniversityPA18015USA

Plant diseases have become a challenging threat in the agricultural *** learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases ***,deep learning entails extensive data for training,and it may be challenging to collect plant *** though plant datasets can be collected,they may be uneven in *** a result,the problem of classification model overfitting *** study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available ***,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image ***,a gradient penalty mechanism was added to the loss function to improve the training stability of the *** show that the proposed method can best improve the recognition accuracy of the classifier with the doubled *** AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the ***,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original ***,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this *** general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.

关键词： Deep learning ACGAN CA gradient penalty tomato diseases identification

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Automated Pneumonia Detection using deep features in chest X-ray images 3

Automated Pneumonia Detection using deep features in chest X...

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3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Ouleddroun, Taoufik Ellahyani, Ayoub El Ansari, Mohamed University of Moulay Ismail Faculty of Sciences Informatics and Applications Laboratory Department of Computer Science Meknès50050 Morocco Ibn Zohr University LabSIV Multidisciplinary Faculty Department of Computer Science Ouarzazate Morocco Ibn Zohr University LabSIV Faculty of Sciences Department of Computer Science Agadir80000 Morocco

ISBN: (纸本)9781665453578

Pneumonia is swelling of the lungs that is usually caused by an infection. This disease is considered as one of the most common reasons for US children to be hospitalized. According to American Thoracic Society (ATS), the cost of treating pneumonia cases in hospitals reached 9.5 billion dollar. The appropriate treatment and recovery process for this disease are linked to early diagnosis. In this work a novel method is proposed for detecting the pneumonia and help the radiologists in their decision making process. First, histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are calculated for chest X-ray images. Then, the images extracted are fed to a model consisting of two stream of Convolutional Neural Networks (CNN) that was trained on the Pneumonia Kermany dataset. Finally, several machine learning classifiers are employed to perform the detection process based on the deep features extracted. The proposed system achieves 97.86% in terms of accuracy on the Kermany dataset, which is satisfactory when compared to recently published works. © 2023 IEEE.

关键词： Decision making

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Bias and Diversity in Synthetic-based Face Recognition

Bias and Diversity in Synthetic-based Face Recognition

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Marco Huber Anh Thi Luu Fadi Boutros Arjan Kuijper Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Synthetic data is emerging as a substitute for authentic data to solve ethical and legal challenges in handling authentic face data. The current models can create real-looking face images of people who do not exist. However, it is a known and sensitive problem that face recognition systems are susceptible to bias, i.e. performance differences between different demographic and non-demographics attributes, which can lead to unfair decisions. In this work, we investigate how the diversity of synthetic face recognition datasets compares to authentic datasets, and how the distribution of the training data of the generative models affects the distribution of the synthetic data. To do this, we looked at the distribution of gender, ethnicity, age, and head position. Furthermore, we investigated the concrete bias of three recent synthetic-based face recognition models on the studied attributes in comparison to a baseline model trained on authentic data. Our results show that the generator generate a similar distribution as the used training data in terms of the different attributes. With regard to bias, it can be seen that the synthetic-based models share a similar bias behavior with the authentic-based models. However, with the uncovered lower intra-identity attribute consistency seems to be beneficial in reducing bias.

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Finger Width Effects on Performance of GaN HEMTs 2

Finger Width Effects on Performance of GaN HEMTs

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2nd International Microwave and Antenna Symposium, IMAS 2024

作者： Nouri, Soheil Avval, Amirreza G. Darwish, Ali El-Ghazaly, Samir M. University of Arkansas Department of Electrical Engineering and Computer Science Fayetteville United States Army Research Laboratory AdelphiMD United States

ISBN: (纸本)9798350356359

In this study, we characterize GaN-HEMT devices and present both DC and AC measurement outcomes for the multi-finger configurations. Specifically, we introduce a new device featuring wide electrodes to assess and compare the performance of various multi-finger arrangements. This particular transistor comprises two fingers, each measuring 150 μm in width. We analyze and compare the s-parameters of structures with varying widths, extending our frequency range up to 67 GHz. This newly developed wide-finger configuration is integral to our ongoing efforts in developing optimized modeling approaches and improved transistor designs. The comparative analysis of different finger widths lays the groundwork for future explorations into high-frequency phenomena, including wave propagation effects and their impact on the performance of millimeter-wave transistors. © 2024 IEEE.

关键词： Gallium nitride

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Enhancing Transportation Mode Prediction from GPS Trajectories using Machine Learning and Feature Engineering

Enhancing Transportation Mode Prediction from GPS Trajectori...

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2023 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2023

作者： Kabiri, Hichame Ghanou, Youssef Khalifi, Hamid Casalino, Gabriella Moulay Ismail University of Meknes Laboratory Ia Meknes Morocco Mohammed v University in Rabat Faculty of Sciences Department of Computer Science Rabat Morocco University of Bari Department of Computer Science Bari Italy

ISBN: (纸本)9798350313352

This manuscript proposes a comprehensive framework for the automated determination of travel modes based solely on GPS trajectories. To improve prediction accuracy, additional preprocessing features are introduced, including speed, acceleration, jerk, and bearing rate. Our approach employs various machine learning techniques, such as Random Forest, Multilayer Perceptron (MLP), AdaBoost Classifier, Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), to achieve notable classification results. Extensive evaluations demonstrate that our framework surpasses existing state-of-the-art algorithms for transport mode prediction. This investigation presents a promising approach for accurate and reliable prediction of travel modes, with potential applications in various real-world scenarios.. © 2023 IEEE.

关键词： Forecasting

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Astrometric Reduction of Saturnian Satellites with Cassini-ISS Images Degraded by Trailed Stars

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Research in Astronomy and Astrophysics 2023年第3期23卷 85-92页

作者： Qing-Feng Zhang Meng-Qi Liu Yan Li Lin-Peng Wu Zhi-Qiang Wang Li-Sha Zhu Zhan Li Department of Computer Science Jinan UniversityGuangzhou 510632China Sino-French Joint Laboratory for Astrometry Dynamics and Space ScienceJinan UniversityGuangzhou 510632China Department of Financial Technology School of ManagementGuangzhou Xinhua UniversityGuangzhou 510520China

The Imaging science Subsystem(ISS)mounted on the Cassini spacecraft has taken a lot of images,which provides an important source of high-precision astrometry of some planets and ***,some of these images are degraded by trailed ***,these degraded images cannot be used for *** this paper,a new method is proposed to detect and compute the centers of these trailed stars *** method is then performed on the astrometry of ISS images with trailed ***,we provided 658 astrometric positions between 2004 and 2017 of several satellites that include Enceladus,Dione,Tethys,Mimas and *** with the JPL ephemeris SAT427,the mean residuals of these measurements are 0.11 km and 0.26 km in *** decl.,*** standard deviations are 1.08 km and 1.37 km,*** results show that the proposed method performs astrometric measurements of Cassini ISS images with trailed stars effectively.

关键词： stars trail Saturn

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