Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices ...Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers.Nevertheless,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers.Additionally,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for training.In this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training models.As a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning process.WebFLex is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous devices.Furthermore,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central server.WebFLex has actually been measured in various setups using the MNIST dataset.Experimental results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data aggregation.In addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network variability.Additionally,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.展开更多
变电站Web发布系统实现了变电站远程监视功能，其中还包括保护定值、采样值和录波波形等信息的查看。文章介绍了CSC2000变电站监控系统中变电站Web发布系统的功能和实现方法，它采用B/S(Browser/Server)结构，其远程浏览功能可以灵活地...变电站Web发布系统实现了变电站远程监视功能，其中还包括保护定值、采样值和录波波形等信息的查看。文章介绍了CSC2000变电站监控系统中变电站Web发布系统的功能和实现方法，它采用B/S(Browser/Server)结构，其远程浏览功能可以灵活地嵌入电力管理信息系统(Management Information System, MIS)，具有使用灵活方便、安全性高的特点，可以作为变电站运行监视的有益补充。展开更多
WebGPS是一套基于网页的车辆管理系统,它提供了一种从网页上远程访问,并实时显示车辆当前行驶位置的手段。构建一个这样的WebGPS系统,必须要有一个提供地图应用服务的平台。分析了整个系统的模型,比较了多种地图服务平台,指出了MapXtrem...WebGPS是一套基于网页的车辆管理系统,它提供了一种从网页上远程访问,并实时显示车辆当前行驶位置的手段。构建一个这样的WebGPS系统,必须要有一个提供地图应用服务的平台。分析了整个系统的模型,比较了多种地图服务平台,指出了MapXtreme for Java作为地图服务器的优势,最后说明了MapXtreme系统的开发方法及功能实现。展开更多
基金This work has been funded by King Saud University,Riyadh,Saudi Arabia,through Researchers Supporting Project Number(RSPD2024R857).
文摘Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers.Nevertheless,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers.Additionally,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for training.In this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training models.As a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning process.WebFLex is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous devices.Furthermore,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central server.WebFLex has actually been measured in various setups using the MNIST dataset.Experimental results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data aggregation.In addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network variability.Additionally,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.
文摘变电站Web发布系统实现了变电站远程监视功能，其中还包括保护定值、采样值和录波波形等信息的查看。文章介绍了CSC2000变电站监控系统中变电站Web发布系统的功能和实现方法，它采用B/S(Browser/Server)结构，其远程浏览功能可以灵活地嵌入电力管理信息系统(Management Information System, MIS)，具有使用灵活方便、安全性高的特点，可以作为变电站运行监视的有益补充。
文摘WebGPS是一套基于网页的车辆管理系统,它提供了一种从网页上远程访问,并实时显示车辆当前行驶位置的手段。构建一个这样的WebGPS系统,必须要有一个提供地图应用服务的平台。分析了整个系统的模型,比较了多种地图服务平台,指出了MapXtreme for Java作为地图服务器的优势,最后说明了MapXtreme系统的开发方法及功能实现。
