一引言
90年代初,一种新型的计算机网络应用技术--电子数据交换EDI(Electronic Data Interchange)以其特有的简洁、高效、安全和迅捷特性引起世界各国的高度重视,被认为是提高工作效率、服务质量和企业竞争能力的强有力的手段[1].EDI旨...一引言
90年代初,一种新型的计算机网络应用技术--电子数据交换EDI(Electronic Data Interchange)以其特有的简洁、高效、安全和迅捷特性引起世界各国的高度重视,被认为是提高工作效率、服务质量和企业竞争能力的强有力的手段[1].EDI旨在实现表单传送的电子化,所以有人称EDI为无纸化贸易.使用电子表单的同时仍然需要纸张表单辅助,只是纸张表单从先前的主要或唯一的地位,下降到次要和辅助的地位.也就是说,EDI最重要的意义不在于节约纸张,而在于其快速、避免重复劳动、提高效率、节约成本等方面,因此EDI技术的实质是强调快速传输(比如从邮寄的几天变成几分钟甚至实时)、节约劳动(不必反复打印和录入表单),从而提高效率和节约成本.展开更多
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
文摘一引言
90年代初,一种新型的计算机网络应用技术--电子数据交换EDI(Electronic Data Interchange)以其特有的简洁、高效、安全和迅捷特性引起世界各国的高度重视,被认为是提高工作效率、服务质量和企业竞争能力的强有力的手段[1].EDI旨在实现表单传送的电子化,所以有人称EDI为无纸化贸易.使用电子表单的同时仍然需要纸张表单辅助,只是纸张表单从先前的主要或唯一的地位,下降到次要和辅助的地位.也就是说,EDI最重要的意义不在于节约纸张,而在于其快速、避免重复劳动、提高效率、节约成本等方面,因此EDI技术的实质是强调快速传输(比如从邮寄的几天变成几分钟甚至实时)、节约劳动(不必反复打印和录入表单),从而提高效率和节约成本.
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.
