Sharing data while protecting privacy in the industrial Internet is a significant challenge.Traditional machine learning methods require a combination of all data for training;however,this approach can be limited by d...Sharing data while protecting privacy in the industrial Internet is a significant challenge.Traditional machine learning methods require a combination of all data for training;however,this approach can be limited by data availability and privacy concerns.Federated learning(FL)has gained considerable attention because it allows for decentralized training on multiple local datasets.However,the training data collected by data providers are often non-independent and identically distributed(non-IID),resulting in poor FL performance.This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain technology.To overcome the problem of non-IID data leading to poor training accuracy,we propose dynamically updating the local model based on the divergence of the global and local models.This approach can significantly improve the accuracy of FL training when there is relatively large dispersion.In addition,we design a dynamic gradient clipping algorithm to alleviate the influence of noise on the model accuracy to reduce potential privacy leakage caused by sharing model parameters.Finally,we evaluate the performance of the proposed scheme using commonly used open-source image datasets.The simulation results demonstrate that our method can significantly enhance the accuracy while protecting privacy and maintaining efficiency,thereby providing a new solution to data-sharing and privacy-protection challenges in the industrial Internet.展开更多
It is the development trend of library information management,which applies the mature and cutting-edge information technology to library information retrieval.In order to realize the rapid retrieval of massive book i...It is the development trend of library information management,which applies the mature and cutting-edge information technology to library information retrieval.In order to realize the rapid retrieval of massive book information,this paper proposes a book retrieval method combining QR code with image retrieval technology.This method analyzes the visual features of book images,design a book image retrieval method based on boundary contour and regional pixel distribution features,and realizes the association retrieval of book information combined with the QR code,so as to improve the efficiency of book retrieval.The experimental results show that,the books can be retrieved effectively through the boundary contour and regional pixel distribution features,the book information can be displayed through QR code,readers can be provided with fast and intelligent massive book retrieval services.展开更多
基金This work was supported by the National Key R&D Program of China under Grant 2023YFB2703802the Hunan Province Innovation and Entrepreneurship Training Program for College Students S202311528073.
文摘Sharing data while protecting privacy in the industrial Internet is a significant challenge.Traditional machine learning methods require a combination of all data for training;however,this approach can be limited by data availability and privacy concerns.Federated learning(FL)has gained considerable attention because it allows for decentralized training on multiple local datasets.However,the training data collected by data providers are often non-independent and identically distributed(non-IID),resulting in poor FL performance.This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain technology.To overcome the problem of non-IID data leading to poor training accuracy,we propose dynamically updating the local model based on the divergence of the global and local models.This approach can significantly improve the accuracy of FL training when there is relatively large dispersion.In addition,we design a dynamic gradient clipping algorithm to alleviate the influence of noise on the model accuracy to reduce potential privacy leakage caused by sharing model parameters.Finally,we evaluate the performance of the proposed scheme using commonly used open-source image datasets.The simulation results demonstrate that our method can significantly enhance the accuracy while protecting privacy and maintaining efficiency,thereby providing a new solution to data-sharing and privacy-protection challenges in the industrial Internet.
文摘It is the development trend of library information management,which applies the mature and cutting-edge information technology to library information retrieval.In order to realize the rapid retrieval of massive book information,this paper proposes a book retrieval method combining QR code with image retrieval technology.This method analyzes the visual features of book images,design a book image retrieval method based on boundary contour and regional pixel distribution features,and realizes the association retrieval of book information combined with the QR code,so as to improve the efficiency of book retrieval.The experimental results show that,the books can be retrieved effectively through the boundary contour and regional pixel distribution features,the book information can be displayed through QR code,readers can be provided with fast and intelligent massive book retrieval services.
