IF 3.4 3区综合性期刊

Sensors Pub Date : 2025-02-06 DOI: 10.3390/s25030973

Sameh Abdelazim, David Santoro, Fred Moshary

{"title":"FPGA Programming Challenges When Estimating Power Spectral Density and Autocorrelation in Coherent Doppler Lidar Systems for Wind Sensing.","authors":"Sameh Abdelazim, David Santoro, Fred Moshary","doi":"10.3390/s25030973","DOIUrl":"10.3390/s25030973","url":null,"abstract":"In this paper, we present the logic designs of two FPGA hardware programming algorithms implemented for a Coherent Doppler Lidar system used in wind sensing. The first algorithm divides the received time-domain signals into segments, each corresponding to a specific spatial resolution. It then calculates the power spectrum for each segment and accumulates these spectra over 10,000 pulse returns. The second algorithm computes the autocorrelation of the received signals and accumulates the results over the same number of pulses. Both signal pre-processing algorithms are initially developed as logic designs and compiled using the Xilinx System Generator toolset to produce a hardware VLSI image. This image is subsequently programmed into an FPGA. However, the hardware implementation of these algorithms presents several challenges: (1) bit growth: multiplication operations in the binary number system significantly increase the number of bits, complicating hardware implementation. (2) Memory constraints: onboard RAM arrays of sufficient size are lacking for accumulating vectors of the calculated Fast Fourier Transforms (FFTs) or autocorrelations. (3) Signal drive issues: large fan-out in the logic design leads to significant capacitance, restricting the driving capabilities of transistor output signals. This article discusses the solutions devised to overcome these challenges. Additionally, it presents atmospheric wind measurements obtained using the two algorithms.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11821183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Prestressed Concrete Cylinder Pipe Broken Wire Detection Algorithm Based on Improved YOLOv5.

IF 3.4 3区综合性期刊

Sensors Pub Date : 2025-02-06 DOI: 10.3390/s25030977

Haoze Li, Ruizhen Gao, Fang Sun, Yv Wang, Baolong Ma

{"title":"A Prestressed Concrete Cylinder Pipe Broken Wire Detection Algorithm Based on Improved YOLOv5.","authors":"Haoze Li, Ruizhen Gao, Fang Sun, Yv Wang, Baolong Ma","doi":"10.3390/s25030977","DOIUrl":"10.3390/s25030977","url":null,"abstract":"The failure accidents of prestressed concrete cylinder pipe (PCCP) seriously affect the economic feasibility of the construction site. The traditional method of needing to stop construction for pipe inspection is time-consuming and laborious. This paper studies the PCCP broken wire identification algorithm based on deep learning. A PCCP wire-breaking test platform was built; the Distributed Fiber Acoustic Sensing Monitoring System (DAS) monitors wire-breakage events in DN4000mm PCCPs buried underground. The collected broken wire signal creates a time-frequency spectrum diagram dataset of the simulated broken wire signal through continuous wavelet transform (CWT). Considering the location of equipment limitations, based on the YOLOv5 algorithm, a lightweight algorithm, YOLOv5-Break is proposed for broken wire monitoring. Firstly, MobileNetV3 is used to replace the YOLOv5 network backbone, and Dynamic Conv is used to replace Conv in C3 to reduce redundant computation and memory access; the coordinate attention mechanism is integrated into the C3 module to make the algorithm pay more attention to location information; at the same time, CIOU is replaced by Focal_EIoU to make the algorithm pay more attention to high-quality samples and balance the uneven problem of complex and easy examples. The YOLOv5-Break algorithm achieves a mAP of 97.72% on the self-built broken wire dataset, outperforming YOLOv8, YOLOv9, and YOLOv10. Notably, YOLOv5-Break reduces the model weight to 7.74 MB, 46.25% smaller than YOLOv5 and significantly lighter than YOLOv8s and YOLOv9s. With a computational cost of 8.3 GFLOPs, YOLOv5-Break is 71.0% and 78.5% more efficient than YOLOv8s and YOLOv9s. It can be seen that the lightweight algorithm YOLOv5-Break proposed in this article simplifies the algorithm without losing accuracy. Moreover, the lightweight algorithm does not require high hardware computing power and can be better arranged in the PCCP broken wire monitoring system.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11820710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Short-Term Traffic Flow Prediction Method Based on Personalized Lightweight Federated Learning.

IF 3.4 3区综合性期刊

Sensors Pub Date : 2025-02-06 DOI: 10.3390/s25030967

Guowen Dai, Jinjun Tang

{"title":"A Short-Term Traffic Flow Prediction Method Based on Personalized Lightweight Federated Learning.","authors":"Guowen Dai, Jinjun Tang","doi":"10.3390/s25030967","DOIUrl":"10.3390/s25030967","url":null,"abstract":"Traffic flow prediction can guide the rational layout of land use. Accurate traffic flow prediction can provide an important basis for urban expansion planning. This paper introduces a personalized lightweight federated learning framework (PLFL) for traffic flow prediction. This framework has been improved and enhanced to better accommodate traffic flow data. It is capable of collaboratively training a unified global traffic flow prediction model without compromising the privacy of individual datasets. Specifically, a spatiotemporal fusion graph convolutional network (MGTGCN) is established as the initial model for federated learning. Subsequently, a shared parameter mechanism of federated learning is employed for model training. Customized weights are allocated to each client model based on their data features to enhance personalization during this process. In order to improve the communication efficiency of federated learning, dynamic model pruning (DMP) is introduced on the client side to reduce the number of parameters that need to be communicated. Finally, the PLFL framework proposed in this paper is experimentally validated using LPR data from Changsha city. The results demonstrate that the framework can still achieve favorable prediction outcomes even when certain clients lack data. Moreover, the communication efficiency of federated learning under this framework has been enhanced while preserving the distinct characteristics of each client, without significant interference from other clients.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11820303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing Near-Infrared Probes for Enhanced Breast Cancer Assessment.

IF 3.4 3区综合性期刊

Sensors Pub Date : 2025-02-06 DOI: 10.3390/s25030983

Mohammad Pouriayevali, Ryley McWilliams, Avner Bachar, Parmveer Atwal, Ramani Ramaseshan, Farid Golnaraghi

{"title":"Advancing Near-Infrared Probes for Enhanced Breast Cancer Assessment.","authors":"Mohammad Pouriayevali, Ryley McWilliams, Avner Bachar, Parmveer Atwal, Ramani Ramaseshan, Farid Golnaraghi","doi":"10.3390/s25030983","DOIUrl":"10.3390/s25030983","url":null,"abstract":"Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear charge-coupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2%. By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, cost-effective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11820052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Modeling and Its Impact on Estimation Accuracy for GPS Navigation Filters.

IF 3.4 3区综合性期刊

Sensors Pub Date : 2025-02-06 DOI: 10.3390/s25030972

Dah-Jing Jwo, Ta-Shun Cho, Birhanu Ayalew Demssie

{"title":"Dynamic Modeling and Its Impact on Estimation Accuracy for GPS Navigation Filters.","authors":"Dah-Jing Jwo, Ta-Shun Cho, Birhanu Ayalew Demssie","doi":"10.3390/s25030972","DOIUrl":"10.3390/s25030972","url":null,"abstract":"This study addresses the divergence issues in GPS navigation systems caused by inaccuracies in dynamic modeling and explores solutions using the extended Kalman filter (EKF). Since algorithms such as the Kalman filter (KF) and EKF rely on assumed process models that often deviate from real-world conditions, their performance in real-time applications can degrade. This paper introduces fictitious process noise as an effective remedy to mitigate divergence, demonstrating its benefits through covariance estimation and tuning factors to enhance observability and controllability, particularly for continuous differential GPS (DGPS) access. The study evaluates several motion scenarios, including stationary receivers, straight-line trajectories with constant and varying speeds, and turning trajectories. The inclusion of process noise allows the EKF to adapt to changes in direction and speed without explicitly modeling turning or acceleration dynamics. To ensure robustness, the simulations incorporate a variety of scenarios to assess the statistical reliability and real-world performance of the EKF, ensuring the findings are statistically robust and widely applicable. Simulated receivers were used to evaluate the position (P), position-velocity (PV), and position-velocity-acceleration (PVA) models. The results from both the Ordinary Least-Squares (OLS) and EKF simulations show improved vehicle trajectory tracking and demonstrate the EKF's potential for broader navigation system applications. This paper's novel contribution lies in its thorough analysis of the divergence issues in GPS navigation filter designs due to dynamic modeling inaccuracies, providing a systematic approach to addressing these challenges and offering new insights to improve estimation accuracy.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11819739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Modified Preassigned Finite-Time Control Scheme for Spacecraft Large-Angle Attitude Maneuvering and Tracking.

IF 3.4 3区综合性期刊

Sensors Pub Date : 2025-02-06 DOI: 10.3390/s25030986

Xudong Ma, Yuan Liu, Yi Cheng, Kun Zhao

{"title":"A Modified Preassigned Finite-Time Control Scheme for Spacecraft Large-Angle Attitude Maneuvering and Tracking.","authors":"Xudong Ma, Yuan Liu, Yi Cheng, Kun Zhao","doi":"10.3390/s25030986","DOIUrl":"10.3390/s25030986","url":null,"abstract":"This paper addresses the problem of large-angle attitude maneuvering and tracking control for rigid spacecraft, considering angular velocity and torque constraints, actuator faults, and external disturbances. First, a sliding-mode-like vector is constructed to guarantee the satisfaction of the angular velocity constraints. A modified preassigned finite-time function, which can adaptively adjust the boundaries, is then proposed to constrain the sliding-mode-like vector. The controller is designed to stabilize the closed-loop system using a barrier Lyapunov function. Additionally, actuator saturation is compensated adaptively, and the system's lumped disturbance is estimated using a fixed-time disturbance observer. Finally, the practically preassigned finite-time stability of the closed-loop system is demonstrated. In practical applications, the proposed controller can guarantee transient and steady-state performance, prevent excessive angular velocity, and ensure compliance with the physical limitations of the actuators. Simulation results are provided to demonstrate the effectiveness of the proposed controller.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11821070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio Approach.

IF 3.4 3区综合性期刊

Sensors Pub Date : 2025-02-06 DOI: 10.3390/s25030976

Jingyun Yang, Pan Li, Yiwen Cui, Xu Han, Mengjie Zhou

{"title":"Multi-Sensor Temporal Fusion Transformer for Stock Performance Prediction: An Adaptive Sharpe Ratio Approach.","authors":"Jingyun Yang, Pan Li, Yiwen Cui, Xu Han, Mengjie Zhou","doi":"10.3390/s25030976","DOIUrl":"10.3390/s25030976","url":null,"abstract":"Accurate prediction of the Sharpe ratio, a key metric for risk-adjusted returns in financial markets, remains a significant challenge due to the complex and stochastic nature of stock price movements. This paper introduces a novel deep learning model, the Temporal Fusion Transformer with Adaptive Sharpe Ratio Optimization (TFT-ASRO), designed to address this challenge. The model incorporates real-time market sensor data and financial indicators as input signals, leveraging multiple data streams including price sensors, volume sensors, and market sentiment sensors to capture the complete market state. Using a comprehensive dataset of US historical stock prices and earnings data, we demonstrate that TFT-ASRO outperforms traditional methods and existing deep learning models in predicting Sharpe ratios across various time horizons. The model's multi-task learning framework, which simultaneously predicts returns and volatility, provides a more nuanced understanding of risk-adjusted performance. Furthermore, our adaptive optimization approach effectively balances the trade-off between return maximization and risk minimization, leading to more robust predictions. Empirical results show that TFT-ASRO achieves a 18% improvement in Sharpe ratio prediction accuracy compared to state-of-the-art baselines, with particularly strong performance in volatile market conditions. The model also demonstrates superior uncertainty quantification, providing reliable confidence intervals for its predictions. These findings have significant implications for portfolio management and investment strategy optimization, offering a powerful tool for financial decision-makers in the era of data-driven investing.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11820675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A PUF-Based Secure Authentication and Key Agreement Scheme for the Internet of Drones.

IF 3.4 3区综合性期刊

Sensors Pub Date : 2025-02-06 DOI: 10.3390/s25030982

Jihye Choi, Seunghwan Son, Deokkyu Kwon, Youngho Park

{"title":"A PUF-Based Secure Authentication and Key Agreement Scheme for the Internet of Drones.","authors":"Jihye Choi, Seunghwan Son, Deokkyu Kwon, Youngho Park","doi":"10.3390/s25030982","DOIUrl":"10.3390/s25030982","url":null,"abstract":"The Internet of Drones (IoD) is an emerging industry that offers convenient services for humans due to the high mobility and flexibility of drones. The IoD substantially enhances human life by enabling diverse drone applications across various domains. However, a malicious adversary can attempt security attacks because communication within an IoD environment is conducted through public channels and because drones are vulnerable to physical attacks. In 2023, Sharma et al. proposed a physical unclonable function (PUF)-based authentication and key agreement (AKA) scheme for the IoD. Regrettably, we discover that their scheme cannot prevent impersonation, stolen verifier, and ephemeral secret leakage (ESL) attacks. Moreover, Sharma et al.'s scheme cannot preserve user untraceability and anonymity. In this paper, we propose a secure and lightweight AKA scheme which addresses the shortcomings of Sharma et al.'s scheme. The proposed scheme has resistance against diverse security attacks, including physical capture attacks on drones, by leveraging a PUF. Furthermore, we utilize lightweight operations such as hash function and XOR operation to accommodate the computational constraints of drones. The security of the proposed scheme is rigorously verified, utilizing \"Burrows-Abadi-Needham (BAN) logic\", \"Real-or-Random (ROR) model\", \"Automated Validation of Internet Security Protocols and Application (AVISPA)\", and informal analysis. Additionally, we compare the security properties, computational cost, communication cost, and energy consumption of the proposed scheme with other related works to evaluate performance. As a result, we determine that our scheme is efficient and well suited for the IoD.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11820592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lightweight CNN-Based Visual Perception Method for Assessing Local Environment Complexity of Unmanned Surface Vehicle.

IF 3.4 3区综合性期刊

Sensors Pub Date : 2025-02-06 DOI: 10.3390/s25030980

Tulin Li, Xiufeng Zhang, Yingbo Huang, Chunxi Yang

{"title":"Lightweight CNN-Based Visual Perception Method for Assessing Local Environment Complexity of Unmanned Surface Vehicle.","authors":"Tulin Li, Xiufeng Zhang, Yingbo Huang, Chunxi Yang","doi":"10.3390/s25030980","DOIUrl":"10.3390/s25030980","url":null,"abstract":"Addressing the problem of inadequate environmental detection in the process of optimizing search for unmanned surface vehicles (USVs) by a heuristic algorithm, this paper proposes a comprehensive visual perception method that combines a lightweight convolutional neural network (CNN) with the USV's real-time heading angle. This method employs a multi-feature input CNN with residual learning blocks, which takes both the current local environmental images and heading angle features as inputs to identify the complexity of the local environment with higher accuracy and a smaller load size. Meanwhile, human expertise is incorporated to classify labels through a majority voting system, thereby making the model's perceptual classification more intuitive and allowing it to possess a human-like comprehensive perception ability compared to systems with classification methods with several parameters. Subsequently, this identification result can be used as feedback for the heuristic algorithm to optimize and plan the USV's path. The simulation results indicate that the developed model achieves an 80% reduction in model size while maintaining an accuracy exceeding 90%. The proposed method significantly improves the environment recognition capability of the heuristic algorithm, enhances optimization search efficiency, and increases the overall performance of path planning by approximately 21%.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11819720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Collaborative Learning in the Industrial IoT Using Federated Learning and Adaptive Weighting Based on Shapley Values.

IF 3.4 3区综合性期刊

Sensors Pub Date : 2025-02-06 DOI: 10.3390/s25030969

Dost Muhammad Saqib Bhatti, Mazhar Ali, Junyong Yoon, Bong Jun Choi

{"title":"Efficient Collaborative Learning in the Industrial IoT Using Federated Learning and Adaptive Weighting Based on Shapley Values.","authors":"Dost Muhammad Saqib Bhatti, Mazhar Ali, Junyong Yoon, Bong Jun Choi","doi":"10.3390/s25030969","DOIUrl":"10.3390/s25030969","url":null,"abstract":"The integration of the Industrial Internet of Things (IIoT) and federated learning (FL) can be a promising approach to achieving secure and collaborative AI-driven Industry 4.0 and beyond. FL enables the collaborative training of a global model under the supervision of a central server while ensuring that data remain localized to ensure data privacy. Subsequently, the locally trained models can be aggregated to enhance the global model training process. Nevertheless, the merging of these local models can significantly impact the efficacy of global training due to the diversity of each industry's data. In order to enhance robustness, we propose a Shapley value-based adaptive weighting mechanism that trains the global model as a sequence of cooperative games. The client weights are adjusted based on their Shapley contributions as well as the size and variability of their local datasets in order to improve the model performance. Furthermore, we propose a quantization strategy to mitigate the computational expense of Shapley value computation. Our experiments demonstrate that our method achieves the highest accuracy compared to existing methods due to the efficient assignment of weights. Additionally, our method achieves nearly the same accuracy with significantly lower computational cost by reducing the computation overhead of Shapley value computation in each round of training.","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11820426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143410430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensors最新文献