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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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Dual defense:Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks

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Journal of Information and Intelligence 2025年第1期3卷 68-90页

作者： Jun Niu Peng Liu Chunhui Huang Yangming Zhang Moxuan Zeng Kuo Shen Yangzhong Wang Suyu An Yulong Shen Xiaohong Jiang Jianfeng Ma He Wang Gaofei Wu Anmin Fu Chunjie Cao Xiaoyan Zhu Yuqing Zhang School of Computer Science and Technology Xidian UniversityXi'an 710071China Information Sciences and Technology Pennsylvania State UniversityPA 16802USA School of Cyberspace Security Hainan UniversityHaikou 570228China School of Hangzhou Institute of Technology Xidian UniversityHangzhou 311231China School of Systems Information Science Future University of HakodateHakodate 041-8655Japan School of Cyber Engineering Xidian UniversityXi'an 710071China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Telecommunications Engineering Xidian UniversityXi'an 710071China School of Computer&Control Engineering University of Chinese Academy of SciencesBeijing 101408China

Membership inference(MI)attacks threaten user privacy through determining if a given data example has been used to train a target *** MI defenses protect the membership privacy through preemptive exclusion of members techniques and knowledge ***,using either of these two defenses alone,the defense effect can still offers an unsatisfactory trade-off between membership privacy and *** that the defense method that directly combines these two defenses is still very limited(e.g.,the test accuracy of the target model is decreased by about 40%(in our experiments)),in this work,we propose a dual defense(DD)method that includes the preemptive exclusion of high-risk member samples module and the knowledge distillation module,which thwarts the access of the resulting models to the private training data twice to mitigate MI *** defense method can be divided into two steps:the preemptive exclusion of high-risk member samples(Step 1)and the knowledge distillation to obtain the protected student model(Step 2).We propose three types of exclusions:existing MI attacks-based exclusions,sample distances of members and nonmembers-based exclusions,and mutual information value-based exclusions,to preemptively exclude the high-risk member *** the knowledge distillation phase,we add ground-truth labeled data to the reference dataset to decrease the protected student model's dependency on soft labels,aiming to maintain or improve its test *** evaluation shows that DD significantly outperforms state-of-the-art defenses and offers a better privacy-utility *** example,DD achieves∼100%test accuracy improvement over the distillation for membership privacy(DMP)defense for ResNet50 trained on *** simultaneously achieves the reductions in the attack effectiveness(e.g.,the TPR@0.01%FPR of enhanced MI attacks decreased by 2.10%on the ImageNet dataset,the membership advantage(MA)of risk score-based attacks d

关键词： Machine learning Membership inference defenses Preemptive exclusion Knowledge distillation

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Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study

arXiv

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arXiv 2025年

作者： Fard, Ali Samimi Mashhadigholamali, Mohammadreza Zolfaghari, Samaneh Abedi, Hajar Chakraborty, Mainak Borzì, Luigi Daneshtalab, Masoud Shaker, George Department of Control and Computer Engineering Polytechnic University of Turin Turin Italy School of Innovation Design and Engineering Division of Intelligent Future Technologies Mälardalen University Västerås Sweden Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

Human Activity Recognition has gained significant attention due to its diverse applications, including ambient assisted living and remote sensing. Wearable sensor-based solutions often suffer from user discomfort and reliability issues, while video-based methods raise privacy concerns and perform poorly in low-light conditions or long ranges. This study introduces a Frequency-Modulated Continuous Wave radar-based framework for human activity recognition, leveraging a 60 GHz radar and multi-dimensional feature maps. Unlike conventional approaches that process feature maps as images, this study feeds multi-dimensional feature maps—Range-Doppler, Range-Azimuth, and Range-Elevation —as data vectors directly into the machine learning (SVM, MLP) and deep learning (CNN, LSTM, ConvLSTM) models, preserving the spatial and temporal structures of the data. These features were extracted from a novel dataset with seven activity classes and validated using two different validation approaches. The ConvLSTM model outperformed conventional machine learning and deep learning models, achieving an accuracy of 90.51% and an F1-score of 87.31% on cross-scene validation, and an accuracy of 89.56% and an F1-score of 87.15% on leave-one-person-out cross-validation. The results highlight the approach’s potential for scalable, non-intrusive, and privacy-preserving activity monitoring in real-world scenarios. © 2025, CC BY-NC-ND.

关键词： Deep learning

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Multi-channel transmission meta-holograms enabled by a single-layer complex-amplitude metasurface

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Optics Letters 2025年第8期50卷 2542-2545页

作者： Zhu, Lei Wang, Yiya Dong, Liang Shang, Guanyu Burokur, Shah Nawaz Ding, Xumin School of Computer and Control Engineering Qiqihar University Qiqihar161006 China Communication and Electronics Engineering Institute Qiqihar University Qiqihar161006 China Key Laboratory of Big Data Network Security Detection and Analysis in Heilongjiang Qiqihar University Qiqihar161006 China Innovation Photonics and Imaging Center Instrumentation Science and Engineering Harbin Institute of Technology Harbin150001 China LEME UPL Univ. Paris Nanterre Ville d'AvrayF92410 France Advanced Microscopy and Instrumentation Research Center Harbin Institute of Technology Harbin150080 China

With an increasing demand for wireless communications, it is difficult for traditional communication technologies to achieve high-quality multi-channel wireless transmissions. Complex-amplitude metasurfaces provide an effective method to deal with this issue by jointly controlling the amplitude and phase of electromagnetic (EM) waves. However, the limitations of single-layer transmission efficiency and amplitude-phase coupling make it challenging to achieve multi-channel high-quality transmission and system integration. Here, a single-layer complex-amplitude metasurface design strategy for multi-channel high-quality transmission is proposed. The amplitude and phase of each transmission channel can be concurrently controlled by adjusting the structural parameters and rotation angles of the resonators. The metasurface successfully reconstructs different holograms in five transmission channels. All experimental results align well with the calculated and simulated results, demonstrating an innovative solution for multi-channel high-quality information transmission, with significant potential applications in the fields of wireless communications. © 2025 Optica Publishing Group.

关键词： Holograms

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Machine Learning for Detecting Steering in Qutrit-Pair States

arXiv

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arXiv 2025年

作者： Wang, Pu Li, Zhongyan Meng, Huixian School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Mathematics and Physics North China Electric Power University Beijing102206 China

Only a few states in high-dimensional systems can be identified as (un)steerable using existing theoretical or experimental methods. We utilize semidefinite programming (SDP) to construct a dataset for steerability detection in qutrit-qutrit systems. For the full-information feature F1, artificial neural networks achieve high classification accuracy and generalization, and preform better than the support vector machine. As feature engineering playing a pivotal role, we introduce a steering ellipsoid-like feature F2, which significantly enhances the performance of each of our models. Given the SDP method provides only a sufficient condition for steerability detection, we establish the first rigorously constructed, accurately labeled dataset based on theoretical foundations. This dataset enables models to exhibit outstanding accuracy and generalization capabilities, independent of the choice of features. As applications, we investigate the steerability boundaries of isotropic states and partially entangled states, and find new steerable states. This work not only advances the application of machine learning for probing quantum steerability in high-dimensional systems but also deepens the theoretical understanding of quantum steerability itself. Copyright © 2025, The Authors. All rights reserved.

关键词： Support vector machines

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Non-contact all-optic OCT–PAM imaging with shared detection light

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Applied Optics 2025年第13期64卷 3431-3437页

作者： Ding, Ning Xiang, Ben Jiang, Huiwen Yue, Jinyun Yu, Yao Liu, Jian Zhao, Yuqian Luan, Jingmin Yang, Yanqiu Wang, Yi Ma, Zhenhe College of Information Science and Engineering Northeastern University Shenyang 110819 China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004 China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004 China

We proposed a non-contact all-optic OCT–PAM dual-modal system based on a single detection light source. A homodyne low-coherence interferometer was adopted to detect the vibration induced by photoacoustic excitation. The optical coherence tomography (OCT) and photoacoustic microscopy (PAM) shared the same broadband light source for signal detection, thus simplifying the dual-modal system and making it compact. Using the proposed system, we performed absorption-scattering imaging of human hairs, measured the femoral artery and vein flow rates in mice, and imaged the auricle blood vessels in mice in vivo. The combination of OCT and PAM can provide complementary biological tissue information and offer a richer reference for the diagnosis of diseases. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Biomedical imaging Fabry Perot interferometers Imaging systems Imaging techniques Optical imaging Tissue imaging

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SPMNet: A Siamese Pyramid Mamba Network for Very-High-Resolution Remote Sensing Change Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Wang, Jiashu Song, Jinze Zhang, Hao Zhang, Zekai Ji, Yunlong Zhang, Wenyin Zhang, Jinglin Wang, Xing Linyi University School of Computer Science and Engineering Linyi276000 China Shandong University Department of Control Science and Engineering Jinan250061 China Linyi University School of Information Science and Engineering China

Very-High-Resolution (VHR) remote sensing images are characterized by extremely high spatial resolution, incorporating higher pixel density and larger image sizes, which pose challenges for existing methods to extract complex texture features. Furthermore, due to the wide-area and high-resolution imaging strategy, VHR change detection images suffer from a severe imbalance between change pixels and non-change pixels, increasing the difficulty of handling change detection tasks. To address these challenges, we introduced the Omnidirectional Selective Scan Module (OSSM), which has the capability to process long sequences. By integrating it with the lightweight Siamese Feature Pyramid Network (SFPN), we designed a hybrid CNN-Mamba backbone, referred to as SPMamba. This backbone captures both global and local information within bitemporal feature maps at each stage, enhancing the precision of texture feature extraction. Additionally, to integrate the semantic features from each branch in SPMamba and reduce noise interference from non-target change areas, we developed a Hybrid Fusion Module (HFM). The HFM consists of two fusion modules: the High-Low Channel Fusion Module (HLM) and the Bilateral Channel Fusion Module (BCM), which facilitates both feature-level and channel-level integration, enhancing the sensitivity of the model to subtle changes. Extensive experimental results demonstrate that SPMNet achieves the highest F1-score of 91.80%, 90.99%, and 96.04% on the WHU-CD, LEVIR-CD, and CDD-CD datasets, respectively, outperforming eleven state-of- the-art methods. Moreover, the effects of varying image sizes on model training are thoroughly analyzed. © 1980-2012 IEEE.

关键词： Change detection

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GrasOpen: Biometric Authentication via Reach-and-Grasp for Smart Door Access Using Smartwatch

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IEEE Internet of Things Journal 2025年

作者： Ding, Lei Shi, Jiale Wu, Yuan Hu, Xinrong Bi, Hongliang Chen, Yanjiao Wuhan Textile University School of Computer Science and Artificial Intelligence Engineering Research Center of Hubei Province for Clothing Information 430200 China China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang University College of Electrical Engineering Hangzhou310027 China

In the domain of smart devices, biometric identity authentication has become a leading and crucial technology, mainly because of its improved security and user convenience. Traditional methods frequently depend on complex activities, facial recognition, or passwords, which can be cumbersome and error-prone. This paper presents GrasOpen, an innovative biometric authentication system tailored for door-opening scenarios, utilizing the natural reach-and-grasp motion linked to door handles. The system employs smartwatches with accelerometers and gyroscopes to track and analyze arm movements, ensuring a seamless and intuitive user experience. GrasOpen tackles key challenges by leveraging the unique characteristics of the reach-and-grasp motion, eliminating the necessity for users to remember complex motions, and avoiding redundant actions like waiting for facial recognition. Specifically, GrasOpen initially proposes a lightweight model to discern door-opening actions from daily activities. Then, to realize the robust authentication system, GrasOpen integrates a complementary filter (CF) method to capture diversity-dependent features in door-opening actions. These features are subsequently processed through a modified ConvBoost model for precise user authentication. Experimental results reveal an impressive accuracy of 98.78% for activity recognition and 98.65% for identity authentication, highlighting GrasOpen’s excellence in function and performance. The system’s security and robustness are validated across diverse authentication environments. © 2014 IEEE.

关键词： Biometrics

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Cluster-Refined Optimal Transport for Unsupervised Action Segmentation

Cluster-Refined Optimal Transport for Unsupervised Action Se...

引用

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

作者： Shijie Wang Jinrong Zhang Yule Liu Shiyao Li Lin Feng School of Computer Science and Technology Dalian University of Technology Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China School of Innovation and Entrepreneurship Dalian University of Technology Dalian China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Action segmentation in untrimmed videos is essential for comprehensive video understanding. Despite significant progress in unsupervised methods, capturing both long-range dependencies and short-duration actions simultaneously remains a challenging task. To address this challenge, this paper introduces the Cluster-Refined Optimal Transport (CROT) method, combining hierarchical clustering and optimal transport for unsupervised action segmentation. We first hierarchically cluster video frame representations to capture long-range dependencies and generate pseudo-boundaries. Initial pseudo-labels are then obtained via optimal transport, ensuring short-duration actions are recognized. Finally, these pseudo-labels are refined using the pseudo-boundaries, resulting in the final segmentation output. Extensive experiments on three public datasets, i.e., YouTube Instructions, Breakfast, and 50Salads, demonstrate that our method performs on par with or better than previous approaches.

关键词： Video on demand Signal processing Acoustics Web sites Speech processing Videos

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MASS FLOW DETECTION TECHNOLOGY FOR SEED AND FERTILISER PARTICLES AND ITS APPLICATION IN UAV-BASED SPREADING

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INMATEH - Agricultural engineering 2025年第1期75卷 584-599页

作者： Wang, Yanshuang Song, Cancan Gao, Yongqi Lan, Yubin Wang, Guobin Ming, Rui College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology Zibo China Research of Institute of Ecological Unmanned Farm Shandong University of Technology Zibo China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

The processes of sowing and fertiliser application represent a significant aspect of agricultural production. In order to achieve efficient and precise seeding and fertiliser application, mass flow detection of seed and fertiliser particles can facilitate real-time monitoring and precise decision-making for intelligent seeding and fertiliser application. However, the diversity of seed and fertiliser particle types and particle flow modes presents a challenge for existing detection methods, particularly in meeting the varying operational requirements, especially for particle mass flow detection in low-altitude and high-speed sowing operations of agricultural drones. In such cases, the overlap and flow rate will have a significant impact on the detection results due to the large displacement and the continuous high-throughput dense phase of the particle flow. This paper provides a summary of the existing seed and fertiliser particle mass flow detection techniques and their underlying working principles. It compares the direct detection based on mass with the indirect detection methods based on velocity and concentration, and analyses their respective advantages, disadvantages and applicability. It also explores the possibility of optimising the existing detection methods for the specific needs of agricultural UAVs and considers the potential introduction of cutting-edge science and technology in order to develop an efficient, accurate and convenient detection system to meet the growing market demand. © (2025), (INMA Bucharest). All Rights Reserved.

关键词： Precision agriculture

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