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
目的 设计一个基于移动物联网(Mobile Internet of Things,MIoT)的健康管理平台,实现医疗设备的智能化管理。方法 基于MIoT的健康管理平台构建由感知层、网络层、平台层以及应用层组成的系统架构,感知层通过三维加速传感器与射频识别标...目的 设计一个基于移动物联网(Mobile Internet of Things,MIoT)的健康管理平台,实现医疗设备的智能化管理。方法 基于MIoT的健康管理平台构建由感知层、网络层、平台层以及应用层组成的系统架构,感知层通过三维加速传感器与射频识别标签实现数据采集,网络层运用5G切片技术结合无线入侵检测系统和无线网络控制器传输数据,云平台集成实时流处理与批量分析引擎,应用层通过智能算法实现医疗设备的智能化管理。比较基于MIoT的健康管理平台应用前后医疗设备调配次数、设备调配响应时间、调配差错台数、设备平均维修周期、设备终末维护合格率、运维支出成本以及维修维保金额。结果 基于MIoT的健康管理平台应用后,医疗设备使用率、医疗设备调配次数、设备终末维护合格率与平台应用前比较均显著提升,差异有统计学意义(P<0.05),设备调配响应时间、调配差错台数、设备平均维修周期、运维支出成本、维修维保金额均显著降低,差异有统计学意义(P<0.05)。结论 基于MIoT的健康管理平台在医疗设备智能化管理中能够显著提升医疗设备使用效率,减少医疗设备的维护成本,为医院医疗设备的智能化管理提供参考。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.
文摘目的 设计一个基于移动物联网(Mobile Internet of Things,MIoT)的健康管理平台,实现医疗设备的智能化管理。方法 基于MIoT的健康管理平台构建由感知层、网络层、平台层以及应用层组成的系统架构,感知层通过三维加速传感器与射频识别标签实现数据采集,网络层运用5G切片技术结合无线入侵检测系统和无线网络控制器传输数据,云平台集成实时流处理与批量分析引擎,应用层通过智能算法实现医疗设备的智能化管理。比较基于MIoT的健康管理平台应用前后医疗设备调配次数、设备调配响应时间、调配差错台数、设备平均维修周期、设备终末维护合格率、运维支出成本以及维修维保金额。结果 基于MIoT的健康管理平台应用后,医疗设备使用率、医疗设备调配次数、设备终末维护合格率与平台应用前比较均显著提升,差异有统计学意义(P<0.05),设备调配响应时间、调配差错台数、设备平均维修周期、运维支出成本、维修维保金额均显著降低,差异有统计学意义(P<0.05)。结论 基于MIoT的健康管理平台在医疗设备智能化管理中能够显著提升医疗设备使用效率,减少医疗设备的维护成本,为医院医疗设备的智能化管理提供参考。
