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arXiv 2025年

作者： Li, Yang Xia, Chunhe Li, Chang Li, Xiaojian Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing100191 China Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China Guangxi Collaborative Innovation Center Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China College of Computer Science and Information Technology Guangxi Normal University Guilin541004 China School of Cyber Science and Technology Beihang University Beijing100191 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

Federated learning based on homomorphic encryption has received widespread attention due to its high security and enhanced protection of user data privacy. However, the characteristics of encrypted computation lead to three challenging problems: "computation-efficiency", "attack-tracing" and "contribution-assessment". The first refers to the efficiency of encrypted computation during model aggregation, the second refers to tracing malicious attacks in an encrypted state, and the third refers to the fairness of contribution assessment for local models after encryption. This paper proposes a federated learning storage security model with homomorphic encryption (FLSSM) to protect federated learning model privacy and address the three issues mentioned above. First, we utilize different nodes to aggregate local models in parallel, thereby improving encrypted models’ aggregation efficiency. Second, we introduce trusted supervise nodes to examine local models when the global model is attacked, enabling the tracing of malicious attacks under homomorphic encryption. Finally, we fairly reward local training nodes with encrypted local models based on trusted training time. Experiments on multiple real-world datasets show that our model significantly outperforms baseline models in terms of both efficiency and security metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Image-Text Retrieval with Intra-Modal Soft Labels

SSRN

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SSRN 2022年

作者： Zhang, Yu Liu, Haoliang Xiong, Wei Mi, Siya Bian, Yali School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China School of Cyber Science and Engineering Southeast University Nanjing211189 China Purple Mountain Laboratories Nanjing China Intel Labs United States

Image-text retrieval attempts to find relevant images from a huge corpus of images given a textual description, and vice versa. Existing works usually denote the negative/positive image-text pair with a binary label. They assume that a text is only relevant to the paired images and irrelevant to other images in the corpus. However, using the binary labels cannot reveal the precise similarity between an image and a text. In this paper, we learn the model using soft labels, where each image-text pair is associated with a specific soft label. We generate the Intra-modal Soft Label (ISL) between the cross-modal (image, text) pair by the similarity between the intra-modal (image caption, text) pair. Systematic experiments on public benchmarks, including MSCOCO and Flickr30K, demonstrate the effectiveness of the proposed method, which achieves a recall@1 improvement of approximately 1%-3% compared to the baseline method. © 2022, The Authors. All rights reserved.

关键词： information retrieval

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A Semi-Supervised Deep network Embedding Approach Based on the Neighborhood Structure

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Big Data Mining and Analytics 2019年第3期2卷 205-216页

作者： Wenmao Wu Zhizhou Yu Jieyue He School of Computer Science and Engineering Southeast UniversityNanjing 211100China MOE Key Laboratory of Computer Network and Information Integration Southeast UniversityNanjing 211100China

network embedding is a very important task to represent the high-dimensional network in a lowdimensional vector space,which aims to capture and preserve the network *** existing network embedding methods are based on shallow ***,actual network structures are complicated which means shallow models cannot obtain the high-dimensional nonlinear features of the network *** recently proposed unsupervised deep learning models ignore the labels *** address these challenges,in this paper,we propose an effective network embedding method of Structural Labeled Locally Deep Nonlinear Embedding(SLLDNE).SLLDNE is designed to obtain highly nonlinear features through utilizing deep neural network while preserving the label information of the nodes by using a semi-supervised classifier component to improve the ability of ***,we exploit linear reconstruction of neighborhood nodes to enable the model to get more structural *** experimental results of vertex classification on two real-world network datasets demonstrate that SLLDNE outperforms the other state-of-the-art methods.

关键词： network embedding deep learning network analysis

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A parameter-independent algorithm of finding maximum clique with Seidel continuous-time quantum walks

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iScience 2024年第2期27卷 108953页

作者： Li, Xi Chen, Xiao Hu, Shouwei Xu, Juan Liu, Zhihao School of Computer Science and Engineering Southeast University No.2 Sipailou District Nanjing 210096 Jiangsu China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education No.2 Southeast University Road Jiangning District Nanjing 211189 Jiangsu China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics No.29 Junjun Dadao Jiangning District Nanjing 210016 Jiangsu China Collaborative Innovation Center of Novel Software Technology and Industrialization No.29 Junjun Dadao Jiangning District Nanjing 210023 Jiangsu China

The maximum clique (MC) problem holds significance in network analysis. Quantum-based algorithms have recently emerged as promising approaches for this problem. However, these algorithms heavily depend on parameters of quantum system and vary significantly for different graphs. In order to tackle this issue, we initially demonstrate that continuous-time quantum walks (CTQW) driven by the Seidel matrix offer valuable insights into the clique structure of graphs, outperforming the CTQW driven by adjacency matrix. Specifically, we conduct an in-depth analysis for CTQW of 4 types of graphs, meticulously calculating the amplitudes associated with different vertices. Our findings consistently reveal that vertices belonging to MC exhibit the highest intensity at the largest frequency component of the probability amplitude for these types of graphs. Considering the varying intensities, we propose a parameter-independent algorithm for determining the MC. We compare our algorithm with a typical quantum-based algorithm, the results indicate that our algorithm exhibits greater stability. © 2024 The Authors

关键词： Physics Quantum physics

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Computing Power in the Sky: Digital Twin-assisted Collaborative Computing with Multi-UAV networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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Sequential-Scanning Dual-Energy CT Imaging Using High Temporal Resolution Image Reconstruction and Error-Compensated Material Basis Image Generation

arXiv

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arXiv 2024年

作者： Li, Qiaoxin Chen, Ruifeng Wang, Peng Quan, Guotao Du, Yanfeng Liang, Dong Li, Yinsheng Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Laboratory of Image Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China CT RPA Department United Imaging Healthcare Company Ltd. Shanghai China Research Center for Medical Artificial Intelligence Key Laboratory of Biomedical Imaging Science and System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Dual-energy computed tomography (DECT) has been widely used to obtain quantitative elemental composition of imaged subjects for personalized and precise medical diagnosis. Compared with DECT leveraging advanced X-ray source and/or detector technologies, the use of the sequential-scanning data acquisition scheme to implement DECT may make a broader impact on clinical practice because this scheme requires no specialized hardware designs and can be directly implemented into conventional CT systems. However, since the concentration of iodinated contrast agent in the imaged subject varies over time, sequentially scanned data sets acquired at two tube potentials are temporally inconsistent. As existing material basis image reconstruction approaches assume that the data sets acquired at two tube potentials are temporally consistent, the violation of this assumption results in inaccurate quantification of material concentration. In this work, we developed sequential-scanning DECT imaging using high temporal resolution image reconstruction and error-compensated material basis image generation, ACCELERATION in short, to address the technical challenge induced by temporal inconsistency of sequentially scanned data sets and improve quantification accuracy of material concentration in sequential-scanning DECT. ACCELERATION has been validated and evaluated using numerical simulation data sets generated from clinical human subject exams and experimental human subject studies. Results demonstrated the improvement of quantification accuracy and image quality using ACCELERATION. Copyright © 2024, The Authors. All rights reserved.

关键词： computerized tomography

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BFLN: A Blockchain-based Federated Learning Model for Non-IID Data

arXiv

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arXiv 2024年

作者： Li, Yang Xia, Chunhe Huang, Dongchi Li, Xiaojian Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing100191 China Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China College of Computer Science and Information Technology Guangxi Normal University Guilin541001 China School of Cyber Science and Technology Beihang University Beijing100191 China

As the application of federated learning becomes increasingly widespread, the issue of imbalanced training data distribution has emerged as a significant challenge. Federated learning utilizes local data stored on different training clients for model training, rather than centralizing data on a server, thereby greatly enhancing the privacy and security of training data. However, the distribution of training data across different clients may be imbalanced, with different categories of data potentially residing on different clients. This presents a challenge to traditional federated learning, which assumes data distribution is independent and identically distributed (IID). This paper proposes a Blockchain-based Federated Learning Model for Non-IID Data (BFLN), which combines federated learning with blockchain technology. By introducing a new aggregation method and incentive algorithm, BFLN enhances the model performance of federated learning on non-IID data. Experiments on public datasets demonstrate that, compared to other state-of-the-art models, BFLN improves training accuracy and provides a sustainable incentive mechanism for personalized federated learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

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Joint Beamforming Design for Integrated Sensing and Communication Systems with Hybrid-Colluding Eavesdroppers

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IEEE Transactions on Communications 2025年

作者： Liu, Meiding Zhou, Zhengchun Shi, Qiao Li, Guyue Liu, Zilong Fan, Pingzhi Lee, Inkyu Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Key Laboratory of Analytical Mathematics and Applications China Southeast University School of Cyber Science and Engineering Nanjing210096 China Purple Mountain Laboratories for Network and Communication Security Nanjing210096 China University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom Southwest Jiaotong University Key Laboratory of Information Coding and Transmission Chengdu611756 China Korea University School of Electrical Engineering Seoul02841 Korea Republic of

In this paper, we consider the physical layer security (PLS) problem for integrated sensing and communication (ISAC) systems in the presence of hybrid-colluding eavesdroppers, where an active eavesdropper (AE) and a passive eavesdropper (PE) collude to intercept the confidential information. To ensure the accuracy of sensing while preventing the eavesdropping, a base station transmits a signal consisting of information symbols and sensing waveform, in which the sensing waveform can be also used as artificial noise to interfere with eavesdroppers. Under this setup, we propose an alternating optimization-based two stage scheme (AO-TSS) for improving the sensing and communication performance. In the first stage, based on the assumptions that the perfect channel state information (CSI) of the AE and statistical CSI of the PE are known, the communication and sensing beamforming problem is formulated with the objective of minimizing the weighted sum of the beampattern matching mean squared error (MSE) and cross-correlation, subject to the secure transmission constraint. To tackle the non-convexity, we propose a semi-definite relaxation (SDR) algorithm and a reduced-complexity zero-forcing (ZF) algorithm. Then, the scenarios are further extended to more general cases with imperfect AE CSI and unknown PE CSI. To further improve the communication performance, the second-stage problem is developed to optimize the secrecy rate threshold under the radar performance constraint. Finally, numerical results demonstrate the superiority of the proposed scheme in terms of sensing and secure communication. © 1972-2012 IEEE.

关键词： Mean square error

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FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional network for Colon Disease Classification

arXiv

引用

arXiv 2022年

作者： Wang, Kai-Ni He, Yuting Zhuang, Shuaishuai Miao, Juzheng He, Xiaopu Zhou, Ping Yang, Guanyu Zhou, Guang-Quan Li, Shuo School of Biological Science and Medical Engineering Southeast University Nanjing China LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China The first affiliated hospital of Nanjing medical university Nanjing China Strasbourg France Department of Medical Biophysics University of Western Ontario LondonON Canada

Reliable automatic classification of colonoscopy images is of great significance in assessing the stage of colonic lesions and formulating appropriate treatment plans. However, it is challenging due to uneven brightness, location variability, inter-class similarity, and intra-class dissimilarity, affecting the classification accuracy. To address the above issues, we propose a Fourier-based Frequency Complex network (FFCNet) for colon disease classification in this study. Specifically, FFCNet is a novel complex network that enables the combination of complex convolutional networks with frequency learning to overcome the loss of phase information caused by real convolution operations. Also, our Fourier transform transfers the average brightness of an image to a point in the spectrum (the DC component), alleviating the effects of uneven brightness by decoupling image content and brightness. Moreover, the image patch scrambling module in FFCNet generates random local spectral blocks, empowering the network to learn long-range and local disease-specific features and improving the discriminative ability of hard samples. We evaluated the proposed FFCNet on an in-house dataset with 2568 colonoscopy images, showing our method achieves high performance outperforming previous state-of-the-art methods with an accuracy of 86.35% and an accuracy of 4.46% higher than the backbone. The project page with code is available at https://***/soleilssss/FFCNet. © 2022, CC BY.

关键词： Complex networks

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Differential Privacy Based on Data Provenance Publishing Method

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Jisuanji Xuebao/Chinese Journal of computers 2020年第3期43卷 573-586页

作者： Ni, Wei-Wei Shen, Tao Yan, Dong Department of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration in Southeast University Ministry of Education Nanjing211189 China

Data provenance describes the mechanism and process of data generation and evolution, which records information about the node module executions used to produce concrete data items, as well as those intermediate data items acting as parameters passed between nodes' executions. Data provenance plays an important role in research and applications of data quality assessment, data recovery and data analysis. With increasingly deepening of data sharing, the need for publishing and sharing workflow of provenance, which is the main representation structure of data provenance, becomes increasingly urgent. However, the provenance workflow often contains private or confidential data. For instance, the node modules included in the provenance workflow, as well as the temporal relations among those nodes, may involve the privacy of the data owner. Direct release of provenance workflow will inevitably bring privacy protection issues. Privacy-preserving data provenance publication becomes an urgent problem to be solved. That is to say, the utility of published provenance workflow needs well maintaining under the premise that data privacy should not be disclosed. The existing research mainly focuses on the maintenance of the local mapping relationship of provenance workflow. For example, privacy protection process is implemented to the provenance workflow, which ensures that the degree of a single node would not be leaked, or sensitive mapping relationship would not be leaked in parallel with effective maintaining of provenance workflow's overall input and output mapping relations. For another important manifestation of provenance workflow's utility, i.e. temporal dependence among those front and corresponding back task nodes of the provenance workflow, the maintenance effect is relatively poor. As for the privacy of adjacent nodes distribution in the provenance workflow, the protection ability of existing methods is also far insufficient. To solve the above problems, the definitio

关键词： Mapping

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