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Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in Non-IID Datasets

Future Internet 2024年第3期16卷 95-95页

作者： Lai, Ying-Hsun Chen, Shin-Yeh Chou, Wen-Chi Hsu, Hua-Yang Chao, Han-Chieh Department of Computer Science and Information Engineering National Taitung University Taitung950309 Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Keelung202301 Taiwan Taiwan Semiconductor Manufacturing Company Hsinchu300096 Taiwan Peking University Shenzhen Graduate School Shenzhen518055 China Department of Electrical Engineering National Dong Hwa University Hualien974301 Taiwan Computer Science and Innovation UCSI University Kuala Lumpur56000 Malaysia Department of Artificial Intelligence Tamkang University New Taipei City251301 Taiwan

Federated learning trains a neural network model using the client’s data to maintain the benefits of centralized model training while maintaining their privacy. However, if the client data are not independently and identically distributed (non-IID) because of different environments, the accuracy of the model may suffer from client drift during training owing to discrepancies in each client’s data. This study proposes a personalized federated learning algorithm based on the concept of multitask learning to divide each client model into two layers: a feature extraction layer and a category prediction layer. The feature extraction layer maps the input data to a low-dimensional feature vector space. Furthermore, the parameters of the neural network are aggregated with those of other clients using an adaptive method. The category prediction layer maps low-dimensional feature vectors to the label sample space, with its parameters remaining unaffected by other clients to maintain client uniqueness. The proposed personalized federated learning method produces faster learning model convergence rates and higher accuracy rates for the non-IID datasets in our experiments. © 2024 by the authors.

关键词： Neural network models

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Real-Time Demonstration of Low-Latency Video Delivery via Hybrid Unicast-Broadcast Networks

Real-Time Demonstration of Low-Latency Video Delivery via Hy...

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International Conference on Network and Service Management

作者： Casper Haems Jeroen van der Hooft Hannes Mareen Peter Steenkiste Glenn Van Wallendael Tim Wauters Filip De Turck Department of Information Technology Ghent University - Imec Ghent Belgium Department of Electronics and Information Systems Ghent University - Imec Ghent Belgium Departments of Computer Science and Electrical and Computer Engineering Carnegie Mellon University Pittsburg United States of America

ISBN: (数字)9783903176669

ISBN: (纸本)9798331505158

In response to the growing demand for low-latency video streaming, this paper presents a demonstration of a hybrid unicast-broadcast video delivery system that combines 5G terrestrial broadcasting with over-the-top (OTT) streaming methods. The demonstration features a scalable setup with an interactive dashboard, allowing users to experiment with various configurations and observe key metrics such as bandwidth usage, packet loss, buffer size, and live latency in real-time. Key techniques include Low-Latency DASH (LL-DASH) for HTTP Adaptive Streaming (HAS), packet recovery (PR) and Forward Error Correction (FEC) for reliability, Temporal Layer Injection (TLI) for enhanced quality, and Common Media Application Format (CMAF) with Chunked Transfer Encoding (CTE) for reduced latency. The demonstration shows that this scalable hybrid approach can effectively reduce unicast bandwidth to nearly 0 Mb/s in scenarios without packet loss on the broadcast network, and achieve similar bandwidth reductions in lossy broadcast networks with appropriate Forward Error Correction (FEC) settings, while maintaining a live latency lower than 1 second. These results demonstrate the system's potential for optimizing multimedia delivery, significantly reducing unicast bandwidth while maintaining low-latency streaming.

关键词： Over-the-top media services Unicast 5G mobile communication Packet loss Bandwidth Forward error correction Real-time systems Multimedia communication Low latency communication Video recording

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Research on Rice Disease Identification Based on Improved ShuffleNet V2 Network

Research on Rice Disease Identification Based on Improved Sh...

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computer Technology and Information science (ISCTIS), International Symposium on

作者： Zhihuan Chen Shenming Gu Minghui Yu Zhaoquan Huang Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China School of Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China College of Mathematics and Computer Science Zhejiang Normal University Jinhua China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

How to efficiently and accurately identify rice diseases plays a crucial role in rice planting and production. Deploying large convolutional neural networks in practical applications is challenging due to their complex model parameters, while lightweight networks often compromise accuracy to reduce model size. In this study, we introduce a lightweight and precise network called GM-ShuffleNet for rice disease recognition. Building upon the ShuffleNet V2 network, we integrated the Ghost module and MCA attention mechanism, adjusted the network structure and number of channels, and replaced the ReLU activation function with Mish. Through comparative experiments, the accuracy of GM-ShuffleNet algorithm proposed in this paper reaches 96.20%, which is superior to ShuffleNet V2 and other excellent lightweight networks. In addition, in terms of model complexity comparison, the number of model parameters and FLOPs decreased by 82.54% and 77.44% respectively compared with the original network.

关键词： Information science Accuracy Attention mechanisms Computational modeling Buildings Production Complexity theory Convolutional neural networks Diseases

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Securing MIMO Wiretap Channel with Learning-Based Friendly Jamming under Imperfect CSI

arXiv

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arXiv 2023年

作者： Tuan, Bui Minh Nguyen, Diep N. Trung, Nguyen Linh Nguyen, Van-Dinh Van Huynh, Nguyen Hoang, Dinh Thai Krunz, Marwan Dutkiewicz, Eryk The School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia The College of Engineering and Computer Science The Center for Environmental Intelligence VinUniversity Vinhomes Ocean Park Hanoi Viet Nam The Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3GJ United Kingdom The Department of Electrical and Computer Engineering The University of Arizona TucsonAZ United States

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g. for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g. cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely AEFJ, to jointly maximize the secrecy rate and minimize the block error rate at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the block error rate, offering a good solution for enhancing communication security in wireless communication systems. © 2023, CC BY.

关键词： Multilayer neural networks

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Catalytic Nanoparticles: An Introduction

arXiv

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arXiv 2025年

作者： Rondón, Jairo Gonzalez-Lizardo, Angel Lugo, Claudio Biomedical & Chemical Engineering Departments Polytechnic University of Puerto Rico San Juan Puerto Rico Department of Electrical and Computer Engineering and Computer Science Polytechnic University of Puerto Rico San Juan Puerto Rico Laboratorio de Cinética y Catálisis Universidad de Los Andes Mérida Venezuela

This study explores the transformative potential of nanocatalysts, emphasizing their pivotal role in catalysis and material science. Key synthesis techniques, including chemical reduction and hybrid methods, are highlighted for their ability to control particle size and enhance stability. Applications in environmental remediation, fuel quality improvement, and renewable energy showcase the broad impact of nanocatalysts. Despite challenges in scalability and stabilization, advancements in bimetallic configurations and electro-steric approaches demonstrate significant progress. This research underscores nanocatalysts’ promise for sustainable industrial processes and global challenges. © 2025, CC BY-NC-ND.

关键词： Nanoclay

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Monocular depth estimation based on deep learning for intraoperative guidance using surface-enhanced Raman scattering imaging

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Photonics Research 2025年第2期13卷 550-560页

作者： ANIWAT JUHONG BO LI YIFAN LIU CHENG-YOU YAO CHIA-WEI YANG A.K.M.ATIQUE ULLAH KUNLI LIU RYAN P.LEWANDOWSKI JACK R.HARKEMA DALEN W.AGNEW YU LEO LEI GARY D.LUKER XUEFEI HUANG WIBOOL PIYAWATTANAMETHA ZHEN QIU Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichigan 48824USA Institute for Quantitative Health Science and Engineering Michigan State UniversityEast LansingMichigan 48824USA Department of Biomedical Engineering Michigan State UniversityEast LansingMichigan 48824USA Department of Chemistry Michigan State UniversityEast LansingMichigan 48824USA Department of Pathobiology and Diagnostic Investigation College of Veterinary MedicineMichigan State UniversityEast LansingMichigan 48824USA Department of Periodontics and Oral Medicine University of MichiganAnn ArborMichigan 48104USA Departments of Radiology and Biomedical Engineering University of MichiganAnn ArborMichigan 48109USA Department of Biomedical Engineering School of EngineeringKing Mongkut’s Institute of Technology Ladkrabang(KMITL)Bangkok 10520Thailand

Imaging of surface-enhanced Raman scattering(SERS) nanoparticles(NPs) has been intensively studied for cancer detection due to its high sensitivity, unconstrained low signal-to-noise ratios, and multiplexing detection capability. Furthermore, conjugating SERS NPs with various biomarkers is straightforward, resulting in numerous successful studies on cancer detection and diagnosis. However, Raman spectroscopy only provides spectral data from an imaging area without co-registered anatomic context.

关键词： raman spectroscopy cancer detection surface enhanced raman scattering imaging intraoperative guidance monocular depth estimation anatomic context deep learning sers nanoparticles

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TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter

arXiv

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arXiv 2023年

作者： Chen, Yiqun T. Zou, James Department of Biomedical Data Science Stanford University StanfordCA94305 United States Departments of Biomedical Data Science Electrical Engineering and Computer Science Stanford University StanfordCA94305 United States

Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos. In this work, we introduce TWIGMA (TWItter Generative-ai images with MetadatA), a comprehensive dataset encompassing over 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text, creation date, number of likes), available at https://***/records/8031785. Through a comparative analysis of TWIGMA with natural images and human artwork, we find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. Additionally, we find that the similarity between a gen-AI image and natural images is inversely correlated with the number of likes. Finally, we observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content such as intricate human portraits, whereas their interest in simple subjects such as natural scenes and animals has decreased. Our findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images. © 2023, CC BY.

关键词： Metadata

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IEEE Transactions on Instrumentation and Measurement 2024年 1-1页

作者： Wu, Jin Wang, Chaoqun Li, Chong Jiang, Yi Zhang, Chengxi Huang, Yulong Qi, Yuhua Xue, Bohuan Jiao, Jianhao Fan, Rui Zhang, Wei Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China School of Control Science and Engineering Shandong University Jinan China Department of Automation and Measurement Ocean University of China Qingdao China Department of Electrical Engineering City University of Hong Kong Hong Kong China School of Internet of Things Jiangnan University Wuxi China Department of Automation Harbin Engineering University Harbin China School of Systems Science and Engineering Sun Yat-Sen University China College of Electronics and Information Engineering Tongji University Shanghai China

Similarity transformation problems are important in robotic instrumentation and computer vision based measurements since in many cases the information of visually observed scene scale is unknown and must be restored for accurate 3-dimensional reconstruction. In existing solvers, the scale is often considered as a scalar, i.e., isotropic, which may be invalid for anisotropic-scale setups. This paper exploits some mathematical coincidences that will lead to efficient solution to these problems. Possible further applications also include hand-eye calibration and structure-from-motion. We revisit pose estimation problems within the framework of similarity transformation, the one that considers scale-stretching, rotation and translation simultaneously. Two major problems are taken into account, i.e., the scale-stretching point-cloud registration and perspective-n-points (PnP). It has been found out that these two problems are quite similar. Moreover, we solve the anisotropic-scale registration problem that is important and is a remaining unsolved one in previous literatures. To compute the globally optimal solution of these non-convex problems, algebraic solution is obtained to compute all local minima using computationally efficient methods. The designed algorithm is deployed for robotic-arm pose estimation. We also extend the algorithm for solving the problem of robust magnetometer calibration. Visual pose experiments verify the superiority of the proposed method compared with representatives, including P3P, Lambda-Twist P3P and EPnP, which can be reproduced by repository in https://***/zarathustr/APnP. IEEE

关键词： Calibration

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Space-Air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges

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China Communications 2022年第2期19卷 90-108页

作者： Huanxi Cui Jun Zhang Yuhui Geng Zhenyu Xiao Tao Sun Ning Zhang Jiajia Liu Qihui Wu Xianbin Cao School of Electronic and Information Engineering Beijing Key Laboratory for Network-Based Cooperative Air application ManagementBeihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China China Mobile Research Institute Beijing 100053China Department of Electrical and Computer Engineering University of WindsorONCanadaN9B 3P4 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China College of Electronics and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210007China

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(AI),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.

关键词： 6G AI DT SAGIN NTN Network architecture

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Inverse identification of dynamically important regions in turbulent flows using three-dimensional convolutional neural networks

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Physical Review Fluids 2023年第9期8卷 094605-094605页

作者： Eric Jagodinski Xingquan Zhu Siddhartha Verma Department of Ocean and Mechanical Engineering Florida Atlantic University Boca Raton Florida 33431 USA Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton Florida 33431 USA Harbor Branch Oceanographic Institute Florida Atlantic University Fort Pierce Florida 34946 USA

Near-wall regions in wall-bounded turbulent flows experience intermittent ejection of slow-moving fluid packets away from the wall and sweeps of faster moving fluid towards the wall. These extreme events play a central role in regulating the energy budget of the boundary layer and are analyzed here with the help of a three-dimensional convolutional neural network (CNN). A CNN is trained on direct numerical simulation data from a periodic channel flow to deduce the intensity of such extreme events and, more importantly, to reveal contiguous three-dimensional salient structures in the flow that are determined autonomously by the network to be critical to the formation and evolution of ejection events. These salient regions, reconstructed using a multilayer gradient-weighted class activation mapping technique proposed here, correlate well with bursting streaks and coherent fluid packets being ejected away from the wall. The focus on explainable interpretation of the network's learned associations also reveals that ejections are not associated with regions where turbulent kinetic energy (TKE) production reaches a maximum, but instead with regions that entail extremely low dissipation and a significantly higher tendency for positive TKE production than negative production. This is a key finding of the study and indicates that CNNs can help reveal dynamically important three-dimensional salient regions using a single scalar-valued metric provided as the quantity of interest, which in the present case is the ejection intensity. While the current work presents an alternate means of analyzing nonlinear spatial correlations associated with near-wall bursts, the framework presented is sufficiently general so as to be extendable to other scenarios where the underlying spatial dynamics are not known a priori.

关键词： Boundary layers Turbulence Convolutional neural networks Deep learning

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