首页|联邦边缘智能网络碳排放评估及优化

联邦边缘智能网络碳排放评估及优化

扫码查看
原文链接
  • 万方数据
  • 维普
近年来,通信技术的持续演进导致通信网络的能耗显著增加.随着人工智能(AI,artificial intelligence)技术与算法在通信网络中的广泛应用和深度部署,未来 6G智能通信网络架构和技术演进将对通信网络的节能减排带来更为严峻的挑战.基于边缘计算和分布式联邦学习的联邦边缘智能(FEI,federated edge intelligence)网络已被普遍认为是实现 6G网络内生智能的关键路径之一.然而,如何评估和优化联邦边缘智能网络的综合碳排放量仍然是一大难题.为解决该问题,首先,提出了一种联邦边缘智能网络碳排放评估框架和方法.其次,基于该评估框架和方法提出 3 种联邦边缘智能网络碳排放优化方案,包括动态能量交易(DET,dynamic energy trading)、动态任务分配(DTA,dynamic task allocation)和动态能量交易与任务分配(DETA,dynamic energy trading and task allocation).最后,通过自行搭建的真实硬件平台,并利用真实世界的碳强度数据集进行联邦边缘智能网络生命周期碳排放仿真.实验结果表明,3 种优化方案均能在不同场景和约束条件下显著减少联邦边缘智能网络的碳排放,为下一代智能通信网络的可持续发展和实现绿色低碳 6G网络提供了依据.
Evaluation and optimization of carbon emission for federal edge intelligence network
In recent years,the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence(AI)technology and algorithms in telecommunication networks,the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intel-ligence(FEI),based on edge computing and distributed federated machine learning,has been widely acknowledged as one of the key pathway for implementing network native intelligence.However,evaluating and optimizing the compre-hensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue,a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subse-quently,three carbon emission optimization schemes for FEI networks were presented,including dynamic energy trading(DET),dynamic task allocation(DTA),and dynamic energy trading and task allocation(DETA).Finally,by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets,FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sus-tainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.

6Gcarbon emissionfederated edge intelligence networkcarbon emission assessment framework and methoddynamic energy trading and task allocation

张鹏、肖泳、胡记伟、廖亮、吕建新、白泽刚

展开 >

华中科技大学,湖北 武汉 430074

烽火通信科技股份有限公司,湖北 武汉 430074

鹏城实验室,广东 深圳 518055

琶洲实验室(黄埔),广东 广州 510555

武汉烽火技术服务有限公司,湖北 武汉 430074

展开 >

6G 碳排放 联邦边缘智能网络 碳排放评估框架和方法 动态能量交易与任务分配

国家自然科学基金湖北省科技创新及人才服务专项武汉市人工智能专项鹏城实验室重大攻关项目

620711932021EHB0152022010702040062PCL2023AS1-2

2024

10.11959/j.issn.2096-3750.2024.00375

物联网学报
人民邮电出版社有限公司

物联网学报

ISSN：2096-3750
年,卷(期)：2024.8(1)
  • 33