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1st Conference on Blockchain and Web3.0 technology Innovation and Application, BWTAC 2024

作者： Zhang, Jinquan Wang, Chongbo Yang, Rendong Tang, Yuncan Ma, Yunshen Ni, Lina College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Key Laboratory of the Ministry of Education for Embedded System and Service Computing Tongji University Shanghai201804 China

ISBN: (纸本)9789819794119

Federated Learning (FL), as one of the effective methods to solve the problem of medical data silos, can promote mutual cooperation among medical institutions under the premise of safeguarding the privacy and security of medical data, and effectively promote the development of intelligent medical treatment. However, the characteristics of medical data with multiple sources and heterogeneity bring certain challenges to the application of federated learning in the medical field. Due to the wide range of medical data sources, some medical institutions are unable to complete the federated learning training task on time due to the limitation of device performance, which leads to the failure of federated learning training. To address the problem of heterogeneity of computing devices among medical institutions that may cause some clients to fall behind, we propose an Adaptive Federated Learning based on Device Performance (AFedDP) method. Specifically, we design an adaptive local model training method to reduce the computational burden of medical institutions with poor equipment performance in the local model training process by adaptively constructing a local training model. Additionally, we propose a hierarchical model aggregation algorithm based on dynamic weight. This allows the heterogeneous adaptive local model to actively participate in the global model aggregation, making full use of the local data of each medical institution and improving the performance of the global model. Finally, we design an aggregation weight update algorithm based on momentum optimization to enhance the stability of global model aggregation. This algorithm updates contribution and aggregation weight with the idea of momentum optimization. Simulation experiments demonstrate that AFedDP achieves higher accuracy compared to the baseline algorithm. AFedDP can better address the issue of some devices being unable to participate in federated training properly. © The Author(s), under exclusive lic

关键词： Federated learning

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Joint DNN Partition and Thread Allocation Optimization for Energy-Harvesting MEC systems

Joint DNN Partition and Thread Allocation Optimization for E...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Yizhou Shi Liying Li Yue Zeng Peijin Cong Junlong Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Deep neural networks (DNNs) have demonstrated exceptional performance, leading to diverse applications across various mobile devices (MDs). Considering factors like portability and environmental sustainability, an increasing number of MDs are adopting energy harvesting (EH) techniques for power supply. However, the computational intensity of DNNs presents significant challenges for their deployment on these resource-constrained devices. Existing approaches often employ DNN partition or offloading to mitigate the time and energy consumption associated with running DNNs on MDs. Nonetheless, existing methods frequently fall short in accurately modeling the execution time of DNNs, and do not consider to use thread allocation for further latency and energy consumption optimization. To solve these problems, we propose a dynamic DNN partition and thread allocation method to optimize the latency and energy consumption of running DNNs on EH-enabled MDs. Specifically, we first investigate the relationship between DNN inference latency and allocated threads and establish an accurate DNN latency prediction model. Based on the prediction model, a DRL-based DNN partition (DDP) algorithm is designed to find the optimal partitions for DNNs. A thread allocation (TA) algorithm is proposed to reduce the inference latency. Experimental results from our test-bed platform demonstrate that compared to four benchmarking methods, our scheme can reduce DNN inference latency and energy consumption by up to 37.3% and 38.5%.

关键词： Energy consumption Instruction sets Heuristic algorithms Artificial neural networks Predictive models Prediction algorithms Inference algorithms Partitioning algorithms Resource management Optimization

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2056-2069页

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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Resource Allocation for MEC in Ultra-dense Networks

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Journal of Computers (Taiwan) 2025年第1期36卷 143-162页

作者： Ma, Huan Li, Shuyun Wang, Zhihui Dou, Xubing Zhang, Xinchang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China

In the complex context of 5G ultra-dense networks, the unprecedented growth in traffic and computing demand has made the interaction between edge devices and edge servers extremely intricate due to the time-varying nature of the network environment and resource heterogeneity. The combination of UDN and Mobile Edge computing not only significantly improves network performance but also expands application scenarios and enhances user experience, marking it as one of the crucial trends in future network development. In this paper, we review recent results and research progress on MEC-based resource allocation in 5G ultra-dense networks. We analyze resource allocation strategies from multiple dimensions, including user association, interference management, energy efficiency optimization, delay reduction, access selection, and user mobility. Finally, we discuss the challenges and open issues that need to be addressed for future development in this area. © 2025 Codon Publications. All rights reserved.

关键词： 5G mobile communication systems

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Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval

Source-free Domain Adaptation with Multiple Alignment for Ef...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinghui Ni Hui Cui Lihai Zhao Fengling Li Xiaohui Han Lijuan Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Quan Cheng Laboratory Shandong Open University University of Technology Sydney

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA.

关键词： Training Data privacy Adaptation models Source coding Semantics Image retrieval Signal processing Protection Speech processing Knowledge transfer

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Outsourced Secure Cross-Modal Retrieval based on Secret Sharing for Lightweight Clients

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IEEE Internet of Things Journal 2025年

作者： Niu, Ziyu Wang, Hao Li, Zhi Su, Ye Xu, Lijuan Zhang, Yudi Susilo, Willy Shandong Normal University School of Information Science and Engineering Jinan China University of Wollongong School of Computing and Information Technology Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education National Supercomputer Center in Jinan Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

Cross-modal retrieval is a technique that uses one modality to query another modality in multimedia data (e.g., retrieving images based on text, or retrieving text based on images). It can break down the barriers between different modalities and achieve seamless information connection. Secure cross-modal retrieval focuses on privacy issues in cross-modal retrieval, including private data of data owners and private query requests of users. Current work on secure cross-modal retrieval protects private information through homomorphic encryption, which makes the efficiency of the retrieval phase not ideal. Therefore, the conflict between retrieval efficiency and security has become an important issue that needs to be resolved in secure cross-modal retrieval. We propose a scheme to achieve secure cross-modal retrieval in the form of secret sharing in the IoT environment. In the scheme, the data owner (DO) can secretly divide all the original data into two parts and upload them to two non-collusive cloud servers respectively. The servers store the data and provide cross-modal retrieval for users. The security of the scheme is proved under semi-honest model, and the experiments show that our scheme is more efficient than previous work in the search phase. When the query dimension is 512 and the number of latent factors is 500, the search time is reduced by more than half compared with previous work. © 2014 IEEE.

关键词： Sensitive data

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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

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HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

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IEEE Transactions on Computers 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong Xidian University School of Computer Science and Technology China Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China Jiangxi University of Finance and Economics School of Computing Nanchang330013 China Jiangxi University of Finance and Economics Nanchang330013 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. © 1968-2012 IEEE.

关键词： Scalability

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