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Build Yourself before Collaboration: Vertical Federated Learning with Limited Aligned Samples

IEEE Transactions on Mobile Computing 2025年第07期24卷 6503-6516页

作者： Shen, Wei Ye, Mang Yu, Wei Yuen, Pong C. Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan China Hong Kong Baptist University Department of Computer Science Hong Kong Hong Kong

Vertical Federated Learning (VFL) has emerged as a crucial privacy-preserving learning paradigm that involves training models using distributed features from shared samples. However, the performance of VFL can be hindered when the number of shared or aligned samples is limited, a common issue in mobile environments where user data are diverse and unaligned across multiple devices. Existing approaches use feature generation and pseudo-label estimation for unaligned samples to address this issue, unavoidably introducing noise during the generation process. In this work, we propose Local Enhanced Effective Vertical Federated Learning (LEEF-VFL), which fully utilizes unaligned samples in the local learning before collaboration. Unlike previous methods that overlook private labels owned by each client, we leverage these private labels to learn from all local samples, constructing robust local models to serve as solid foundations for collaborative learning. Additionally, we reveal that the limited number of aligned samples introduces distribution bias from global data distribution. In this case, we propose to minimize the distribution discrepancies between the aligned samples and the global data distribution to enhance collaboration. Extensive experiments demonstrate the effectiveness of LEEF-VFL in addressing the challenges of limited aligned samples, making it suitable for VFL in mobile computing environments. Codes are available at https://***/shentt67/LEEF-VFL. © 2025 IEEE.

关键词： Federated learning

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Communication-Efficient Federated Learning Over-the-Air With Sparse One-Bit Quantization

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IEEE Transactions on Wireless Communications 2024年第10期23卷 15673-15689页

作者： Oh, Junsuk Lee, Donghyun Won, Dongwook Noh, Wonjong Cho, Sungrae Chung-Ang University School of Computer Science and Engineering Seoul06974 Korea Republic of Hallym University School of Software Chuncheon24252 Korea Republic of

Federated learning (FL) is a framework for realizing distributed machine learning in an environment where training samples are distributed to each device. Recently, FL has employed over-the-air computation enabling all devices to transmit learning model updates simultaneously. This work proposes a communication-efficient sparse one-bit analog aggregation (SOBAA) method, incorporating new power control, layer-wise scaled one-bit quantization, layer-wise sparsification, and an error-feedback mechanism. We derive a tight upper bound of the expected convergence rate of the proposed SOBAA as a closed-form expression. From this expression, we explicitly identify the relationship between the convergence rate and compression and aggregation errors. Based on the theoretical convergence analysis, we formulate a joint optimization problem of the compression ratio and power control to minimize compression and aggregation errors, leading to the fastest convergence. In each communication round, the optimization problem is decomposed, and solved in a computationally efficient and feasible way. From this solution, we characterize the trade-off between learning performance and communication cost. Through extensive experiments on well-known MNIST and CIFAR-10 datasets, we confirm that the proposed method provides an enhanced trade-off performance between test accuracy and communication costs and a faster convergence rate than the other state-of-the-art methods. In addition, it is proven that the proposed method is more effective for more complex datasets and learning models. © 2024 IEEE.

关键词： Fading channels

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Heterologous Image Matching Based on Salience Region

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 894-900页

作者： Zhai, Jinlong Xu, Yang Yan, Xiaohu Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

To address the matching problem caused by the significant differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region is proposed in this paper. Firstly, texture features and corner features are detected. Secondly, the epanechnikov kernel function is used to calculate the point with the maximum kernel function value among all feature points, which is then defined as E-point. Thirdly, the salience region is defined and extracted according to the position of the E-point. Finally, six groups of heterologous images are used as data sources, and compared with HAPCG, HOWP, SIFT, and PSO_SIFT algorithms respectively. The results show that the comprehensive matching performance of the proposed method is better than the original algorithm without salience region processing. The root mean square error and the matching time are less than the original method while ensuring a similar number of matching points, which greatly improves the accuracy and speed of heterologous image matching. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Image matching

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Cardinality estimation for property graph queries with gated learning approach on the graph database

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Multimedia Tools and Applications 2025年第11期84卷 9159-9183页

作者： He, Zhenzhen Yu, Jiong Du, Xusheng Guo, Binglei Li, Ziyang Li, Zhe School of Information Science and Engineering Xinjiang University Urumqi China School of Computer Engineering Hubei University of Arts and Science Xiangyang China School of Software Xinjiang University Urumqi China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the execution of query tasks. Accurate estimate query cardinality is crucial for property graph data model, which usually involves entities, multiple joins, and various types of properties (key-value pairs). While learning-based approaches have been used in query optimization, estimating the cardinality of property graph queries is still particularly challenging. In this paper, we first formally represent each property graph query and then divide the estimation process into three phases: query execution, training data generation, and model training. Secondly, we construct a data pool to deal the updating query workloads in the training data generation phase. Thirdly, we utilize the gate mechanism to develop a cardinality estimation framework based on deep learning neural networks for property graph queries in the Neo4j database, and we propose a hybrid loss function to optimize the training process. Finally, we adopt a method of pre-aggregating the underlying data to speed up query execution in the first phase. The result of the experiment on two real-world datasets shows that our optimized methods can effectively improve the estimation accuracy of the deep learning model, and our query estimator outperforms other deep learning models compared in terms of estimation accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Query processing

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An Efficient and Fast Area Optimization Approach for Mixed Polarity Reed-Muller Logic Circuits

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Chinese Journal of Electronics 2024年第5期33卷 1165-1180页

作者： Yuhao ZHOU Zhenxue HE Jianhui JIANG Xiaojun ZHAO Fan ZHANG Limin XIAO Xiang WANG School of Software Engineering Tongji University Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm(WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm(FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.

关键词： Integrated circuits Microorganisms Annealing Logic circuits Circuits Benchmark testing Delays

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Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems

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Soft Computing 2024年第13-14期28卷 7983-7998页

作者： Umar, Shahla U. Rashid, Tarik A. Ahmed, Aram M. Hassan, Bryar A. Baker, Mohammed Rashad Department of Software College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Department of Computer Science College of Science Charmo University Sulaimani Chamchamal46023 Iraq

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Swarm intelligence

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Music source separation via hybrid waveform and spectrogram based generative adversarial network

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Multimedia Tools and Applications 2024年 1-15页

作者： Wu, Qiuxia Deng, Haipeng Hu, Kun Wang, Zhiyong School of Software Engineering South China University of Technology Guangdong Province Guangzhou China School of Computer Science The University of Sydney SydneyNSW Australia

Music source separation aims to disentangle individual sources from the mixture of musical signals. Existing generative adversarial network (GAN) based methods generally work on the spectrogram domain only. However, this practice ignores the patterns from the waveform domain, which are more informative for modelling some categories of sources. In this paper, we propose a fully hybrid GAN framework to integrate knowledge from both domains. In particular, the generator formulates acoustical patterns from waveform and spectrogram domains, while the discriminator provides discriminative information based on the local patch-level spectrograms such that the generator can produce more plausible separation results. Furthermore, to enhance the quality of estimated sources, we devise a perceptual spectrogram loss term, which is a complement of the waveform-level loss. The proposed method is evaluated on two widely used music source separation datasets, producing music sources of high signal-to-distortion ratio (12.03 in MIR-1K dataset and 8.08 in MUSDB18 dataset). These results demonstrate the superiority of the proposed method compared with the state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Uvcgan-Dehaze: a dehazing method for unpaired images

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Soft Computing 2024年第20期28卷 12217-12226页

作者： Li, Canlin Zhang, Xiangfei Zhang, Wenjiao Su, Haowen Bi, Lihua School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China School of Software Engineering Zhengzhou University of Light Industry Zhengzhou China

To solve the problems of feature loss and color difference after image dehazing and poor dehazing effect in real hazy images, a method UVCGAN-Dehaze is proposed for unpaired image dehazing. In the proposed model, the generator learns the relationship between image features and low-frequency features at multiple scales by combining the U-Net with the transformer. We strengthen the connection between spatial and channel features by adding Convolutional Block Attention Module(CBAM) before the skip connection of each layer of U-Net of the generator and adaptively adjusting the weight allocation of the feature map, so that the image after dehazing can better retain the detailed information of the image. Simultaneously, in order to reduce the feature loss and color difference after image dehazing, we propose a loss function named Feature Laplacian Perception (FLP) loss, which jointly constrains the generator by fusing the perception loss and Laplacian loss, so that the generated image reduces the feature loss and color difference. The method not only generates dehazing images with high perceptual quality in terms of important regions and details, but also avoids content and color loss. Extensive experiments verify the effectiveness of the method on synthetic images and real hazy images, and the proposed method achieves better dehazing results when compared with the experimental results of previous typical methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Color

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Meaningful image encryption algorithm based on compressive sensing and integer wavelet transform

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Frontiers of computer science 2023年第3期17卷 161-175页

作者： Xiaoling HUANG Youxia DONG Guodong YE Yang SHI Faculty of Mathematics and Computer Science Guangdong Ocean UniversityZhanjiang 524088China School of Software Engineering Tongji UniversityShanghai 200092China

A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this *** of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public *** make the chaotic sequence more random,a mathematical model is constructed to improve the random ***,the plain image is compressed and encrypted to obtain the secret ***,the secret image is inserted with numbers zero to extend its size same to the plain *** applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier ***,a meaningful carrier image embedded with secret plain image can be obtained by inverse ***,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic ***,information entropy of the plain image is employed to produce the initial conditions of chaotic *** a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the *** simulations show that the normalized correlation(NC)values between the host image and the cipher image are *** is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.

关键词： image encryption algorithm compressive sensing integer wavelet transform Hadamard matrix

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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