The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital...The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital ecosystems.As massive,distributed data streams are generated across edge devices and network layers,there is a growing need for intelligent,privacy-preserving AI solutions that can operate efficiently at the network edge.Federated Learning(FL)enables decentralized model training without transferring sensitive data,addressing key challenges around privacy,bandwidth,and latency.Despite its benefits in enhancing efficiency,real-time analytics,and regulatory compliance,FL adoption faces challenges,including communication overhead,heterogeneity,security vulnerabilities,and limited edge resources.While recent studies have addressed these issues individually,the literature lacks a unified,cross-domain perspective that reflects the architectural complexity and application diversity of Convergence ICT.This systematic review offers a comprehensive,cross-domain examination of FL within converged ICT infrastructures.The central research question guiding this review is:How can FL be effectively integrated into Convergence ICT environments,and what are the main challenges in implementing FL in such environments,along with possible solutions?We begin with a foundational overview of FL concepts and classifications,followed by a detailed taxonomy of FL architectures,learning strategies,and privacy-preserving mechanisms.Through in-depth case studies,we analyse FL’s application across diverse verticals,including smart cities,healthcare,industrial automation,and autonomous systems.We further identify critical challenges—such as system and data heterogeneity,limited edge resources,and security vulnerabilities—and review state-of-the-art mitigation strategies,including edge-aware optimization,secure aggregation,and adaptive model updates.In addition,we explore emerging directions in FL research,such as energy-efficient learning,federated reinforcement learning,and integration with blockchain,quantum computing,and self-adaptive networks.This review not only synthesizes current literature but also proposes a forward-looking road map to support scalable,secure,and sustainable FL deployment in future ICT ecosystems.展开更多
April 23,global celebration&webcastInternational Girls in ICT Day has been a flagship ITU initiative since 2011,aimed at encouraging girls and young women to pursue studies and careers in information and communica...April 23,global celebration&webcastInternational Girls in ICT Day has been a flagship ITU initiative since 2011,aimed at encouraging girls and young women to pursue studies and careers in information and communication technologies (ICT).It focuses on awareness raising on the opportunities that ICT can provide for educational and socio-economic empowerment of girls and young women.展开更多
在新一代信息技术革命驱动下,ICT(Information and Communications Technology,信息通信技术)制造业产业竞争力越来越受制于供应链韧性,不断整合、构建和重新配置内外部资源,优化生产要素组合是企业增强供应链韧性的有效途径。以2018-2...在新一代信息技术革命驱动下,ICT(Information and Communications Technology,信息通信技术)制造业产业竞争力越来越受制于供应链韧性,不断整合、构建和重新配置内外部资源,优化生产要素组合是企业增强供应链韧性的有效途径。以2018-2022年ICT制造业上市企业为研究样本,选取新型生产要素代理变量,构建供应链韧性评价指标体系,运用面板数据回归模型揭示新型生产要素对ICT制造业供应链韧性的影响。研究发现:(1)数据要素、管理要素对ICT制造业供应链韧性呈显著正向影响,知识要素、技术要素呈显著负向影响,当组合作用时数据要素和知识要素之间存在替代效应,即数据要素水平普遍提升时,知识悖论风险对供应链韧性的负面影响受到一定程度抑制;(2)ICT制造业产业上下游企业供应链韧性存在显著差异,下游企业供应链韧性优于上游企业,且新型要素对上下游供应链韧性具有不同作用;(3)产业地区间竞争格局分化明显,新型要素对供应链韧性的影响存在区域异质性。据此,提出如下建议:促进各要素间协同应用,加强安全管理;促进上下游供应链伙伴间沟通合作,深化供应链各环节协同配套;鼓励中西部地区ICT制造业企业发展,引导新型生产要素在区域之间、产业之间双向流动,培育产业竞争新优势。展开更多
Polymers constitute a series of materials that are essential for many processes such as: food transport, packaging and distribution, construction, etc. Hence, it is important to understand the variables that this proc...Polymers constitute a series of materials that are essential for many processes such as: food transport, packaging and distribution, construction, etc. Hence, it is important to understand the variables that this process depends on. Experimental design is a powerful tool that enables the identification of variables that significantly influence a process through a matrix of experiments that a software constructs. With the aim of implementing ICTs in the learning—teaching process of polymers in a university group, the polymerization process system of PPMA (polymethylmethacrylate) is studied. The variables that affect the size of the Pearl are: temperature, reaction time and volumen of the polyvinyl alcohol aqueous phase. These variables are optimized through a Box-Behnken experimental design with three factors in three levels, achieving the construction of the response surface to optimize the process, obtaining a tridimensional equation that enables the prediction of the Pearl size through the values of the independent variables. Finally, the student acquires the competency of manipulating the variables that affect the polymerization process to optimize the process. For this reason, Design Expert is a valuable ICT tool in the learning-teaching process of polymerization systems.展开更多
文摘The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital ecosystems.As massive,distributed data streams are generated across edge devices and network layers,there is a growing need for intelligent,privacy-preserving AI solutions that can operate efficiently at the network edge.Federated Learning(FL)enables decentralized model training without transferring sensitive data,addressing key challenges around privacy,bandwidth,and latency.Despite its benefits in enhancing efficiency,real-time analytics,and regulatory compliance,FL adoption faces challenges,including communication overhead,heterogeneity,security vulnerabilities,and limited edge resources.While recent studies have addressed these issues individually,the literature lacks a unified,cross-domain perspective that reflects the architectural complexity and application diversity of Convergence ICT.This systematic review offers a comprehensive,cross-domain examination of FL within converged ICT infrastructures.The central research question guiding this review is:How can FL be effectively integrated into Convergence ICT environments,and what are the main challenges in implementing FL in such environments,along with possible solutions?We begin with a foundational overview of FL concepts and classifications,followed by a detailed taxonomy of FL architectures,learning strategies,and privacy-preserving mechanisms.Through in-depth case studies,we analyse FL’s application across diverse verticals,including smart cities,healthcare,industrial automation,and autonomous systems.We further identify critical challenges—such as system and data heterogeneity,limited edge resources,and security vulnerabilities—and review state-of-the-art mitigation strategies,including edge-aware optimization,secure aggregation,and adaptive model updates.In addition,we explore emerging directions in FL research,such as energy-efficient learning,federated reinforcement learning,and integration with blockchain,quantum computing,and self-adaptive networks.This review not only synthesizes current literature but also proposes a forward-looking road map to support scalable,secure,and sustainable FL deployment in future ICT ecosystems.
文摘April 23,global celebration&webcastInternational Girls in ICT Day has been a flagship ITU initiative since 2011,aimed at encouraging girls and young women to pursue studies and careers in information and communication technologies (ICT).It focuses on awareness raising on the opportunities that ICT can provide for educational and socio-economic empowerment of girls and young women.
文摘在新一代信息技术革命驱动下,ICT(Information and Communications Technology,信息通信技术)制造业产业竞争力越来越受制于供应链韧性,不断整合、构建和重新配置内外部资源,优化生产要素组合是企业增强供应链韧性的有效途径。以2018-2022年ICT制造业上市企业为研究样本,选取新型生产要素代理变量,构建供应链韧性评价指标体系,运用面板数据回归模型揭示新型生产要素对ICT制造业供应链韧性的影响。研究发现:(1)数据要素、管理要素对ICT制造业供应链韧性呈显著正向影响,知识要素、技术要素呈显著负向影响,当组合作用时数据要素和知识要素之间存在替代效应,即数据要素水平普遍提升时,知识悖论风险对供应链韧性的负面影响受到一定程度抑制;(2)ICT制造业产业上下游企业供应链韧性存在显著差异,下游企业供应链韧性优于上游企业,且新型要素对上下游供应链韧性具有不同作用;(3)产业地区间竞争格局分化明显,新型要素对供应链韧性的影响存在区域异质性。据此,提出如下建议:促进各要素间协同应用,加强安全管理;促进上下游供应链伙伴间沟通合作,深化供应链各环节协同配套;鼓励中西部地区ICT制造业企业发展,引导新型生产要素在区域之间、产业之间双向流动,培育产业竞争新优势。
文摘Polymers constitute a series of materials that are essential for many processes such as: food transport, packaging and distribution, construction, etc. Hence, it is important to understand the variables that this process depends on. Experimental design is a powerful tool that enables the identification of variables that significantly influence a process through a matrix of experiments that a software constructs. With the aim of implementing ICTs in the learning—teaching process of polymers in a university group, the polymerization process system of PPMA (polymethylmethacrylate) is studied. The variables that affect the size of the Pearl are: temperature, reaction time and volumen of the polyvinyl alcohol aqueous phase. These variables are optimized through a Box-Behnken experimental design with three factors in three levels, achieving the construction of the response surface to optimize the process, obtaining a tridimensional equation that enables the prediction of the Pearl size through the values of the independent variables. Finally, the student acquires the competency of manipulating the variables that affect the polymerization process to optimize the process. For this reason, Design Expert is a valuable ICT tool in the learning-teaching process of polymerization systems.
