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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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How universal polynomial bases enhance spectral graph neural networks: heterophily, over-smoothing, and over-squashing 24

How universal polynomial bases enhance spectral graph neural...

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Proceedings of the 41st International Conference on Machine Learning

作者： Keke Huang Yu Guang Wang Ming Li Pietro Liò School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China and Shanghai AI Laboratory Shanghai China and School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China and Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge UK

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter-based graph neural network - UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on datasets with a diverse range of heterophily, support the superiority of UniBasis in the universality but also its proficiency in graph explanation.

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FAST Observations of an Extremely Active Episode of FRB 20201124A.Ⅲ.Polarimetry

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Research in Astronomy and Astrophysics 2022年第12期22卷 81-98页

作者： Jin-Chen Jiang Wei-Yang Wang Heng Xu Jiang-Wei Xu Chun-Feng Zhang Bo-Jun Wang De-Jiang Zhou Yong-Kun Zhang Jia-Rui Niu Ke-Jia Lee Bing Zhang Jin-Lin Han Di Li Wei-Wei Zhu Zi-Gao Dai Yi Feng Wei-Cong Jing Dong-Zi Li Rui Luo Chen-Chen Miao Chen-Hui Niu Chao-Wei Tsai Fa-Yin Wang Pei Wang Ren-Xin Xu Yuan-Pei Yang Zong-Lin Yang Ju-Mei Yao Mao Yuan Department of Astronomy Peking UniversityBeijing 100871China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Kavli Institute for Astronomy and Astrophysics Peking UniversityBeijing 100871China School of Astronomy University of Chinese Academy of SciencesBeijing 100049China Department of Physics and Astronomy University of NevadaLas VegasNV 89154USA CAS Key Laboratory of FAST NAOCChinese Academy of SciencesBeijing 100101China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-NatalDurban 4000South Africa Department of Astronomy University of Science and Technology of ChinaHefei 230026China Cahill Center for Astronomy and Astrophysics MC 249-17California Institute of TechnologyPasadenaCA 91125USA CSIRO Space and Astronomy P.O.Box 76EppingNSW 1710Australia School of Astronomy and Space Science Nanjing UniversityNanjing 210093China Key Laboratory of Modern Astronomy and Astrophysics(Nanjing University) Ministry of EducationChina South-Western Institute For Astronomy Research Yunnan UniversityYunnan 650504China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China

As the third paper in the multiple-part series,we report the statistical properties of radio bursts detected from the repeating fast radio burst(FRB)source FRB 20201124A with the Five-hundred-meter Aperture Spherical radio Telescope during an extremely active episode between the 25th and 28th of September 2021(UT).We focus on the polarization properties of536 bright bursts with S/N>*** found that the Faraday rotation measures(RMs)monotonically dropped from-579to-605 rad m^(-2)in the 4 day *** RM values were compatible with the values(-300 to-900 rad m^(-2))reported 4 months ***,the RM evolution rate in the current observation window was at least an order of magnitude smaller than the one(~500 rad m^(-2)day^(-1))previously reported during the rapid RM-variation phase,but is still higher than the one(≤1 rad m^(-2)day^(-1))during the later RM no-evolution *** bursts of FRB 20201124A were highly polarized with the total degree of polarization(circular plus linear)greater than 90%for more than 90%of all *** distribution of linear polarization position angles(PAs),degree of linear polarization(L/I)and degree of circular polarization(V/I)can be characterized with unimodal distribution *** the observation window,the distributions became wider with time,i.e.,with larger scatter,but the centroids of the distribution functions remained nearly *** individual bursts,significant PA variations(confidence level 5σ)were observed in 33%of all *** polarization of single pulses seems to follow certain complex trajectories on the Poincarésphere,which may shed light on the radiation mechanism at the source or the plasma properties along the path of FRB propagation.

关键词： (stars:)pulsars:general stars:magnetars radio continuum:general polarization

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zibo Miao Yu Pan Qing Gao Guangdong Key Laboratory of Intelligent Morphing Mechanisms School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique.

关键词： Perturbation methods Design methodology Quantum optics Robustness Feedback control Closed loop systems Tuning Cybernetics

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Scintillation Arc from FRB 20220912A

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Science China(Physics,Mechanics & Astronomy) 2024年第1期67卷 134-142页

作者： Zi-Wei Wu Robert AMain Wei-Wei Zhu Bing Zhang Peng Jiang Jia-Rui Niu Jin-Lin Han Di Li Ke-Jia Lee Dong-Zi Li Yuan-Pei Yang Fa-Yin Wang Rui Luo Pei Wang Chen-Hui Niu Heng Xu Bo-Jun Wang Wei-Yang Wang Yong-Kun Zhang Yi Feng De-Jiang Zhou Yong-Hua Xu Can-Min Deng Yu-Hao Zhu National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Max-Planck-Institut für Radioastronomie Bonn 53121Germany Institute for Frontier in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Nevada Center for Astrophysics and Department of Physics and Astronomy University of NevadaLas VegasNV 89154USA CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Astronomy University of Chinese Academy of SciencesBeijing 100049China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-NatalDurban 4000South Africa Department of Astronomy Peking UniversityBeijing 100871China Kavli Institute for Astronomy and Astrophysics Peking UniversityBeijing 100871China Cahill Center for Astronomy and Astrophysics California Institute of TechnologyPasadena 91125USA South-Western Institute For Astronomy Research Yunnan UniversityKunming 650504China School of Astronomy and Space Science Nanjing UniversityNanjing 210093China Key Laboratory of Modern Astronomy and Astrophysics(Nanjing University) Ministry of EducationNanjing 210023China CSIRO Space and Astronomy Epping 1710Australia Department of Astronomy School of Physics and Materials ScienceGuangzhou UniversityGuangzhou 510006China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China Yunnan Observatories Chinese Academy of SciencesKunming 650011China Guangxi Key Laboratory for Relativistic Astrophysics Department of PhysicsGuangxi UniversityNanning 530004China

We present the interstellar scintillation analysis of fast radio burst(FRB)20220912A during its extremely active episode in 2022using data from the Five-hundred-meter Aperture Spherical Radio Telescope(FAST).We detect a scintillation arc in the FRB’s secondary spectrum,which describes the power in terms of the scattered FRB signals’time delay and Doppler *** arc indicates that the scintillation is caused by a highly localized *** analysis favors a Milky Way origin of the ionized interstellar medium(IISM)for the localized scattering medium but cannot rule out a host galaxy *** present our method for detecting the scintillation arc,which can be applied generally to sources with irregularly spaced bursts or *** methods could help shed light on the complex interstellar environment surrounding the FRBs and in our Galaxy.

关键词： fast radio burst scintillation FRB 20220912A

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How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing

arXiv

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arXiv 2024年

作者： Huang, Keke Wang, Yu Guang Li, Ming Liò, Pietro School of Computing National University of Singapore Singapore Institute of Natural Sciences School of Mathematical Sciences Zhangjiang Institute for Advanced Study Shanghai Jiao Tong University Shanghai China Shanghai AI Laboratory Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science and Technology Cambridge University Cambridge United Kingdom

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitive computations, numerous polynomial filters have been proposed. However, polynomials in the majority of these filters are predefined and remain fixed across different graphs, failing to accommodate the varying degrees of heterophily. Addressing this gap, we demystify the intrinsic correlation between the spectral property of desired polynomial bases and the heterophily degrees via thorough theoretical analyses. Subsequently, we develop a novel adaptive heterophily basis wherein the basis vectors mutually form angles reflecting the heterophily degree of the graph. We integrate this heterophily basis with the homophily basis to construct a universal polynomial basis UniBasis, which devises a polynomial filter-based graph neural network – UniFilter. It optimizes the convolution and propagation in GNN, thus effectively limiting over-smoothing and alleviating over-squashing. Our extensive experiments, conducted on datasets with a diverse range of heterophily, support the superiority of UniBasis in the universality but also its proficiency in graph explanation. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Deformable registration framework for glioma images with absent correspondence based on auxiliary-image-aided intensity-consistency constraint

Deformable registration framework for glioma images with abs...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kun Tang Lihui Wang Menglong Yang Jingwen Xu Xinyu Cheng Jian Zhang Yuemin Zhu Hongjiang Wei Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China INSA Lyon CNRS Inserm Univ Lyon Lyon France School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity.

关键词： Image registration Pathology Deformation Magnetic resonance imaging Anatomical structure Bioinformatics Tumors biomedical imaging

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Gesture recognition based on sparse representation

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International Journal of Wireless and Mobile computing 2016年第4期11卷 348-356页

作者： Miao, Wei Li, Gongfa Sun, Ying Jiang, Guozhang Kong, Jianyi Liu, Honghai College of Machinery and Automation Wuhan University of Science and Technology Wuhan430081 China State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Intelligent Systems and Biomedical Robotics Group School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

Aiming at the problem that the robustness of gesture recognition is difficult to guarantee, this paper presents a method based on multi-features and sparse representation. Hu invariant moments and HOG features of training samples are extracted in training phase. The K-SVD algorithm is used to train the initial value of dictionary formed by two features so as to obtain two sub-dictionaries. In recognition phase, sparse coefficients of corresponding training dictionary are derived by solving minimum l1-norm. Finally, the overall reconstruction error is calculated to judge the categories of test samples. In experimental simulation, five kinds of grasp gesture are collected to create gesture sample library. After selecting optimal HOG parameters and the weight of two features, the recognition effect of the method is analysed. Compared with the commonly used classification, the results show that the method has better recognition rate and robustness. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Gesture recognition

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MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI Cardinality

MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI C...

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Annual Conference on Privacy, Security and Trust, PST

作者： Yihao Yang Xiaolei Dong Jiachen Shen Zhenfu Cao Yunbo Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Zongyang Hou Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan Print-Rite Technology Development Co. Ltd. Of Zhuhai China

Private Set Intersection (PSI) is one of the most important functions in secure multiparty computation (MPC). PSI protocols have been a practical cryptographic primitive and there are many privacy-preserving applications based on PSI protocols such as computing conversion of advertising and distributed computation. Private Set Intersection Cardinality (PSI-CA) is a useful variant of PSI protocol. PSI and PSI-CA allow several parties, each holding a private set, to jointly compute the intersection and cardinality, respectively without leaking any additional information. Nowadays, most PSI protocols mainly focus on two-party settings, while in multiparty settings, parties are able to share more valuable information and thus more desirable. On the other hand, with the advent of cloud computing, delegating computation to an untrusted server becomes an interesting problem. However, most existing delegated PSI protocols are unable to efficiently scale to multiple clients. In order to solve these problems, this paper proposes MDPPC, an efficient PSI protocol which supports scalable multiparty delegated PSI and PSI-CA operations. Security analysis shows that MDPPC is secure against semi-honest adversaries and it allows any number of colluding clients. For 15 parties with set size of 2 20 on server side and 2 16 on clients side, MDPPC costs only 81 seconds in PSI and 80 seconds in PSI-CA, respectively. The experimental results show that MDPPC has high scalability.

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Dual-State Personalized Knowledge Tracing with Emotional Incorporation

arXiv

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arXiv 2024年

作者： Wang, Shanshan Yuan, Fangzheng Wang, keyang Yang, Xun Zhang, Xingyi Wang, Meng Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei230601 China Zhejiang Dahua Technology Co. Ltd China Department of Electronic Engineering and Information Science School of information Science and Technology University of Science and Technology of China Hefei230026 China Key Laboratory of intelligent computing and Signal Processing Ministry of Education school of Computer Science and Technology Anhui University Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Knowledge tracing has been widely used in online learning systems to guide the students' future learning. However, most existing KT models primarily focus on extracting abundant information from the question sets and explore the relationships between them, but ignore the personalized student behavioral information in the learning process. This will limit the model's ability to accurately capture the personalized knowledge states of students and reasonably predict their performances. To alleviate this limitation, we explicitly models the personalized learning process by incorporating the emotions, a representative personalized behavior in the learning process, into KT framework. Specifically, we present a novel Dual-State Personalized Knowledge Tracing with Emotional Incorporation model to achieve this goal: Firstly, we incorporate emotional information into the modeling process of knowledge state, resulting in the Knowledge State Boosting Module. Secondly, we design an Emotional State Tracing Module to monitor students' personalized emotional states, and propose an emotion prediction method based on personalized emotional states. Finally, we apply the predicted emotions to enhance students' response prediction. Furthermore, to extend the generalization capability of our model across different datasets, we design a transferred version of DEKT, named Transfer Learningbased Self-loop model (T-DEKT). Extensive experiments show our method achieves the state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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