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Advancing Medical Innovation through Blockchain-Secured Federated Learning for Smart Health

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Khan, Salabat Khan, Mansoor Khan, Muhammad Asghar Wang, Lu Wu, Kaishun Qilu Institute of Technology School of Computer and Information Engineering Jinan China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan China Prince Mohammad Bin Fahd University Department of Electrical Engineering P.O. Box 1664 Al-Khobar31952 Saudi Arabia Shenzhen University College of Computer Science and Software Engineering Shenzhen China Thrust Guangzhou China

The rapid digitization of healthcare systems has led to a vast accumulation of electronic medical records (EMRs), offering an invaluable source of patient data that can significantly advance medical research and improve patient care. However, sharing EMRs for research purposes presents challenges, particularly concerning data privacy, security, and the limitations of traditional centralized data-sharing models. This paper introduces a novel approach that leverages blockchain technology to facilitate federated learning with EMRs, thereby addressing these challenges. Federated learning enables multiple institutions to collaboratively train a robust machine learning model without sharing raw data, preserving privacy and security. By integrating blockchain, this framework enhances data integrity, immutability, and trust, all in a decentralized environment. The blockchain serves as a transparent and secure ledger, recording model updates and aggregating them through a consensus-based mechanism. Smart contracts further enforce data usage policies, allowing only authorized access and maintaining control over data ownership and sharing. This approach empowers medical researchers and institutions to collaborate more effectively, accelerating the discovery of treatments, advancements in personalized medicine, and insights into rare diseases. It also enables patients to contribute to medical research while retaining control over their personal data, fostering a patient-centered approach to healthcare innovation. Experimental results confirm the efficacy and efficiency of this blockchain-enabled federated learning framework, highlighting its potential to transform medical research and adhere to stringent privacy and security standards. This study emphasizes the pivotal role of blockchain in enhancing big data analytics within healthcare, paving the way for improved collaboration, innovation, and patient outcomes. © 2025 IEEE.

关键词： Federated learning

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Weighted Density for The Win: Accurate Subspace Density Clustering

Weighted Density for The Win: Accurate Subspace Density Clus...

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

作者： Maixuan Peng Yuyang Wu Yang Lu Mengke Li Yiqun Zhang Yiu-Ming Cheung School of Computer Science and Technology Guangdong University of Technology Guangzhou China Faculty of Science Wilfrid Laurier University Brantford Canada Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

k-clustering typically struggles with the detection of irregular-distributed clusters due to the natural bias, while density clustering usually cannot well-adapt to different datasets and clustering tasks as it is not an oriented optimization process. This paper, therefore, proposes to perform density clustering in dynamically learned subspaces. To exploit the irregular-distributed clusters obtained by density clustering for the subspace determination, we design a new strategy to appropriately evaluate the importance of attributes. It turns out that the proposed Weighted Density-based Subspace Clustering (WDSC) algorithm inherits the unbiased merits of density clustering, and also upgrades the unlearning density clustering to be learnable under the subspace learning paradigm of k-clustering. A comprehensive evaluation including significance tests, ablation studies, qualitative comparisons, etc., shows the superiority of WDSC.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Clustering algorithms Signal processing Acoustics Speech processing Optimization

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scMonica: Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis

scMonica: Single-cell Mosaic Omics Nonlinear Integration and...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoli Li Rui Zhang Saba Aslam Huijun Li Yuxi Chen Zequn Zhang Ruey-Song Huang Hongyan Wu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Centre for Cognitive and Brain Sciences University of Macau Macau SAR China Faculty of Science and Technology University of Macau Macau SAR China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China Faculty of Health Sciences University of Macau Macau SAR China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Single-cell mosaic integration has revolutionized our understanding of cellular heterogeneity, offering unprecedented resolution into cellular states and contexts. While large language models (LLMs) have achieved success in the analysis of single-cell omics data, their application specifically focused on the integration of mosaic data remains limited. Current computational approaches often fall short in addressing these challenges. They frequently depend on assumptions of data completeness and uniform quality, failing to manage the variability and noise effectively introduced by missing data. Moreover, these models might not efficiently process large, diverse datasets or account for biological heterogeneity and long-ranging dependencies across various data types. To address these shortcomings, we introduce the Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis (scMonica) framework, which employs a LSTM-transformer hybrid architecture. This innovative model combines the strengths of Long Short-Term Memory (LSTM) networks, which excel at capturing long-range dependencies within sequential gene expression patterns, with transformers, renowned for their attention mechanisms that handle the complex, non-linear interactions characteristic of multi-layered datasets. By leveraging these complementary strengths, our approach enhances the integration process significantly, allowing for nuanced management of the intrinsic heterogeneity and sparsity of mosaic datasets. Comprehensive evaluations demonstrate the robustness and effectiveness of our approach, offering unparalleled versatility and accuracy in multi-omics data analysis. These advancements underscore scMonica’s potential to drive significant insights in single-cell developmental biology, oncology, and beyond. We discuss the underlying technologies, analyze their applications, and contemplate future directions that promise to extend the boundaries of both research and clinical domains.

关键词： Biological system modeling Microprocessors Large language models Noise computer architecture Transformers Oncology Robustness Gene expression Long short term memory

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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A Dual-Band Frequency Selective Surface with Angular Stability at X-band

A Dual-Band Frequency Selective Surface with Angular Stabili...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Lin Du Botao Feng Xiao Ding Li Deng Wenzhe Gu Qingsheng Zeng College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou China Department of Computer and Engineering University of Quebec in Outaouais Quebec Canada

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

This paper proposes a dual-band frequency selective surface (FSS) design. To achieve dual-band functionality, two similarly shaped elements with different sizes are staggered on the substrate. The filtering response is created through strategically designed slots on the middle layer. Three different FSS configurations are designed and analyzed. The inclusion of arms and stubs enhances isolation between the staggered elements, reducing magnetic coupling and strengthening electric coupling, which improves angular stability. The proposed design shows transmission zeros at 7.75 GHz, 9.6 GHz, and 11.75 GHz, with a suppression level of -20 dB, demonstrating its effectiveness in frequency-selective applications.

关键词： Couplings Frequency selective surfaces Program processors Filtering Magnetic separation Dual band Stability analysis Filtering theory Substrates Magnetic properties

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A Secure Multiparty Quantum Homomorphic Encryption Scheme

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computers, Materials & Continua 2022年第11期73卷 2835-2848页

作者： Jing-Wen Zhang Xiu-Bo Chen Gang Xu Heng-Ji Li Ya-Lan Wang Li-Hua Miao Yi-Xian Yang Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Sichuan610025China Quantum Technology Lab&Applied Mechanics Group University of MilanMilan20133Italy Department of Computer Science Faculty of Engineering and Physical SciencesUniversity of SurreyGuildfordSurreyGU27XHUnited Kingdom Huawei Technologies Co.Ltd Shenzhen518129China

The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private *** this paper,a novel secure multiparty quantum homomorphic encryption scheme is proposed,which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest ***,each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and ***,with the help of the almost dishonest server,the non-maximally entangled states are preshared between the client and the server to correct errors in the homomorphic evaluation of T gates,so as to realize universal quantum circuit evaluation on encrypted ***,from the perspective of the application scenario of secure multi-party computation,this work is based on the probabilistic quantum homomorphic encryption scheme,allowing multiple parties to delegate the server to perform the secure homomorphic *** operation and the permission to access the data performed by the client and the server are clearly pointed ***,a concrete security analysis shows that the proposed multiparty quantum homomorphic encryption scheme can securely resist outside and inside attacks.

关键词： Quantum homomorphic encryption secure multiparty computation almost dishonest server security

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Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation

arXiv

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arXiv 2023年

作者： Zhang, Yulong Chen, Shuhao Jiang, Weisen Zhang, Yu Lu, Jiangang Kwok, James T. The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Southern University of Science and Technology Hong Kong University of Science and Technology Hong Kong The Zhejiang Laboratory Hangzhou China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Limited transferability hinders the performance of deep learning models when applied to new application scenarios. Recently, Unsupervised Domain Adaptation (UDA) has achieved significant progress in addressing this issue via learning domain-invariant features. However, the performance of existing UDA methods is constrained by the large domain shift and limited target domain data. To alleviate these issues, we propose DomAin-guided Conditional Diffusion Model (DACDM) to generate high-fidelity and diversity samples for the target domain. In the proposed DACDM, by introducing class information, the labels of generated samples can be controlled, and a domain classifier is further introduced in DACDM to guide the generated samples for the target domain. The generated samples help existing UDA methods transfer from the source domain to the target domain more easily, thus improving the transfer performance. Extensive experiments on various benchmarks demonstrate that DACDM brings a large improvement to the performance of existing UDA methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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On the Impact of Noisy Labels on Supervised Classification Models 23rd

On the Impact of Noisy Labels on Supervised Classificatio...

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23rd International Conference on Computational science, ICCS 2023

作者： Dubel, Rafal Wijata, Agata M. Nalepa, Jakub Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Biomedical Engineering Silesian University of Technology Roosevelta 40 Zabrze41-800 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland

ISBN: (纸本)9783031360206

The amount of data generated daily grows tremendously in virtually all domains of science and industry, and its efficient storage, processing and analysis pose significant practical challenges nowadays. To automate the process of extracting useful insights from raw data, numerous supervised machine learning algorithms have been researched so far. They benefit from annotated training sets which are fed to the training routine which elaborates a model that is further deployed for a specific task. The process of capturing real-world data may lead to acquring noisy observations, ultimately affecting the models trained from such data. The impact of the label noise is, however, under-researched, and the robustness of classic learners against such noise remains unclear. We tackle this research gap and not only thoroughly investigate the classification capabilities of an array of widely-adopted machine learning models over a variety of contamination scenarios, but also suggest new metrics that could be utilized to quantify such models’ robustness. Our extensive computational experiments shed more light on the impact of training set contamination on the operational behavior of supervised learners. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital storage

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iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations

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BMC Biology 2025年第1期23卷 1-14页

作者： Wei, Hang Hou, Jialu Liu, Yumeng Shaytan, Alexey K. Liu, Bin Wu, Hao School of Computer Science and Technology Xidian University Shaanxi Xi’an 710126 China SMBU-MSU-BIT Joint Laboratory On Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Guangdong Shenzhen 518172 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen 518118 China Department of Biology Lomonosov Moscow State University Moscow 119234 Russian Federation International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer Science HSE University Moscow 109028 Russian Federation Zhongguancun Academy Beijing 100094 China

Background: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns. Results: In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and general features of piRNA-disease pairs. Additionally, it integrates their refined and macroscopic inferences to derive the final prediction result. Conclusions: The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance. © The Author(s) 2025.

关键词： Graph ensemble learning Local context graph piRNA-disease association identification

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Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach

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Neural Computing and Applications 2025年 1-33页

作者： Talaat, Fatma M. Shaban, Warda M. ZainEldin, Hanaa Badawy, Mahmoud Elhosseini, Mostafa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and informatics Horus University New Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah42353 Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents a novel eco-assistive fog-based traffic management system (EAFTMS) that leverages a four-tier architecture (IoT, fog, cloud, and application) for scalable, real-time traffic optimization. A key innovation of this system is the AI-driven point exchange system (PES), designed to incentivize sustainable behaviors such as reducing unnecessary vehicle usage and promoting green lifestyle choices. Unlike conventional models, the proposed framework incorporates real-time behavioral monitoring, rewards-based sustainability programs, and V2X-enabled dynamic traffic control. Empirical validation demonstrates that EAFTMS outperforms existing models, including support vector regression (SVR), achieving a 30% reduction in latency, a 40% improvement in response time, a 25% increase in traffic flow efficiency, and a 35% reduction in CO2 emissions. These results highlight the framework’s potential to set new standards in intelligent green cities by offering scalable, practical, and environmentally impactful solutions to urban transportation challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Urban transportation

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