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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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Sarda sarda exploitation status in Western Africa's fishing zone: A case study of the Senegalese bonito population

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Egyptian Journal of Aquatic Research 2025年第2期51卷 225-233页

作者： Sarr, Ousmane Tian, Siquan Sow, Fambaye Ngom Amouri, Rim El Chinwendu, Igbodiegwu Gloria Sadio, Nfally Sadia Petit Gao, Chunxia Kindong, Richard College of Marine Living Resource Sciences and Management Shanghai Ocean University Shanghai 201306 China National Engineering Research Center for Oceanic Fisheries Shanghai 201306 China Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources Ministry of Education Shanghai 201306 China National Data Centre for Distant-water Fisheries of China Shanghai 201306 China Key Laboratory of Oceanic Fisheries Exploration Ministry of Agriculture and Rural Affairs Shanghai 201306 China Trust Services and Consulting Senegal (TSC-SN) Société Anonyme à Responsabilité Limitée (SARL) / DAKAR (Sénégal) Rufisque Arafat 2 villa numéro 250 Senegal Centre de Recherches Océanographiques de Dakar-Thiaroye (CRODT)/Institut Sénégalais de Recherches Agricoles (ISRA) Pôle de Recherches de Hann Dakar BP: 2241 Senegal College of Fisheries and Life Science Shanghai 201306 China College of Economics and Management Shanghai Ocean University Shanghai 201306 China

In West Africa, particularly in Senegalese waters, small tuna species like Atlantic bonito play a central role in marine ecosystems and contribute to food security and livelihoods. However, climate change, illegal fishing, and overfishing have put most of these stocks under pressure, necessitating assessment studies to gauge their exploitation. The research focused on an assessment of the biological parameters of Atlantic bonito (Sarda sarda) and its exploitation conditions in the Senegalese littoral zone. We made a significant effort to collect and analyze approximately 44,700 specimens to gain insights into this species’ key life history and current population state in this western African region. The outcomes, including growth factors such as a = 0.0062 per year, b = 3.2346, and L_∞ = 73.01 cm, provided vital details required by the Length-Based Spawning Potential Ratio model to predict stock level. Moreover, the findings revealed that the size at 50 % and 95 % maturity was 40.75 cm and 56.05 cm, respectively, with ages ranging from 1.5 to 4.6 years, or roughly five years. Meanwhile, the assessment model evaluated the present spawning potential ratio at 0.43, slightly over 40 percent but significantly greater than 20 %, the basic reference points. These results point out that the Sarda sarda stock along the Senegalese fishing coastline is in a sound state, highlighting the possibility of providing significant scientific guidance to inform temporary management measures and ensure its long-term sustainability. © 2025 The Authors

关键词： Assessment Model Sarda sarda Senegalese coastline Sound state

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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A Dynamic Transformer Network for Vehicle Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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Optical fiber triethylamine room temperature gas sensor based on HMS platform with DFT calculation and Experimental Demonstration

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Applied Surface Science 2025年 705卷

作者： Wang, Yan-nan Li, Jin Yuan, Zhenyu Meng, Fanli College of Mechanical and Electronic Engineering Northwest A&F University Yangling 712100 China College of Information Science and Engineering Northeastern University Shenyang 110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China

An optical fiber triethylamine (TEA) gas sensor based on a hollow malposition structure (HMS) platform with CQDs@BiOBr gas-sensing layer is proposed. The CQDs@BiOBr gas-sensing material is coated onto the surface of the hollow core fiber (HCF) using a dip-coating method. The misaligned structure is designed to guide light transmission within the cladding of the HCF and excite the evanescent field. The heterojunction formed between CQDs and BiOBr enhances the separation efficiency of electron-hole pairs, improving the transfer speed of charge carriers during the gas adsorption process. Experimental results show that the sensitivity of sensor at room temperature is 3.53 pm/ppm, which is 2.43 times higher compared to a sensor coated with BiOBr alone. Additionally, the response and recovery times of sensor are 12 s and 36 s, respectively, with a limit of detection (LOD) of 5.67 ppm. DFT calculations provide insight into the electronic transfer pathways and electron cloud distribution during the gas adsorption process, further validating the crucial role of the CQDs@BiOBr heterojunction in enhancing gas-sensing performance. The proposed optical fiber TEA gas sensor shows significant potential for applications in environmental monitoring and industrial safety. © 2025 Elsevier B.V.

关键词： DFT Heterojunction Hollow core fiber Optical fiber sensor TEA gas sensor

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Polynomial-Time Assignment-Based Cell Association with Generic Utility Functions

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China Communications 2022年第9期19卷 214-228页

作者： Lusheng Wang Chao Fang Hai Lin Min Peng Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective *** the one hand,traditional cell association as a non-deterministic polynomial(NP)hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm,which significantly saves computational *** the other hand,the scheme jointly considers utility maximization and load balancing among multiple base stations(BSs)by maintaining an experience pool storing a set of weighting factor values and their corresponding *** an association optimization is required,a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the *** with several representative schemes,the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.

关键词： mobile communication system 2-dimensional assignment problem hungarian algorithm fairness cell association

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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MacroTrend: A Write-Efficient Cache Algorithm for NVM-Based Read Cache

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Journal of Computer Science & Technology 2022年第1期37卷 207-230页

作者： Ning Bao Yun-Peng Chai Xiao Qin Chuan-Wen Wang Key Laboratory of Data Engineering and Knowledge Engineering Ministry of EducationBeijing 100872China School of Information Renmin University of ChinaBeijing 100872China Samuel Ginn College of Engineering Auburn UniversityAlabama 36830U.S.A.

The future storage systems are expected to contain a wide variety of storage media and layers due to the rapid development of NVM(non-volatile memory)*** NVM-based read caches,many kinds of NVM devices cannot stand frequent data updates due to limited write endurance or high energy consumption of ***,traditional cache algorithms have to update cached blocks frequently because it is difficult for them to predict long-term popularity according to such limited information about data blocks,such as only a single value or a queue that reflects frequency or *** this paper,we propose a new MacroTrend(macroscopic trend)prediction method to discover long-term hot blocks through blocks'macro trends illustrated by their access count *** then a new cache replacement algorithm is designed based on the MacroTrend prediction to greatly reduce the write amount while improving the hit *** conduct extensive experiments driven by a series of real-world traces and find that compared with LRU,MacroTrend can reduce the write amounts of NVM cache devices significantly with similar hit ratios,leading to longer NVM lifetime or less energy consumption.

关键词： non-volatile memory(NVM) solid state disk(SSD) cache endurance

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ViGT: proposal-free video grounding with a learnable token in the transformer

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Science China(Information Sciences) 2023年第10期66卷 196-212页

作者： Kun LI Dan GUO Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artificial Intelligence Hefei Comprehensive National Science Center

The video grounding(VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer(ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows.(1) The token is unrelated to the video or the query and avoids data bias toward the original video and query.(2) The token simultaneously performs global context aggregation from video and query ***, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention(i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets:ANet-Captions, TACoS, and YouCookⅡ. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

关键词： video grounding temporal sentence grounding boundary regression token learning proposal-free

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Observer-Based Path Tracking Controller Design for Autonomous Ground Vehicles With Input Saturation

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 749-761页

作者： Heng Wang Tengfei Zhang Xiaoyu Zhang Qing Li School of Automation and Electrical Engineering and also with the Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of EducationUniversity of Science and Technology BeijingBeijing 100083China School of Automation and Electrical Engineering and also with the Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of EducationUniversity of Science and Technology BeijingBeijing 100083

This paper investigates the problem of path tracking control for autonomous ground vehicles(AGVs),where the input saturation,system nonlinearities and uncertainties are ***,the nonlinear path tracking system is formulated as a linear parameter varying(LPV)model where the variation of vehicle velocity is taken into ***,considering the noise effects on the measurement of lateral offset and heading angle,an observer-based control strategy is proposed,and by analyzing the frequency domain characteristics of the derivative of desired heading angle,a finite frequency H_∞index is proposed to attenuate the effects of the derivative of desired heading angle on path tracking ***,sufficient conditions are derived to guarantee robust H_∞performance of the path tracking system,and the calculation of observer and controller gains is converted into solving a convex optimization ***,simulation examples verify the advantages of the control method proposed in this paper.

关键词： Autonomous ground vehicles(AGVs) H_∞index input saturation observer-based controller path tracking control

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