移动通信2026，Vol.50Issue(8)：56-66,11.DOI:10.3969/j.issn.1006-1010.20260605-0005
面向异构AI训练任务的无线通信与计算资源联合调度方法
Joint Wireless Communication and Computing Resource Scheduling for Heterogeneous AI Training Tasks
摘要
Abstract
For edge-device collaborative AI training tasks in wireless networks,scenarios such as vehicular cooperative perception,industrial visual inspection,and background model updating for low-power IoT devices exhibit significant differences in split-layer feature size,computational load,device capability,and SLA constraints.Edge-side collaborative training therefore needs to perform resource scheduling under limited wireless bandwidth and edge computing resources.Due to device computing heterogeneity and wireless channel fluctuation,non-cooperative training or static resource allocation methods can hardly meet diverse training requirements,which may lead to latency violations or inefficient resource utilization.To address this issue,this paper proposes a joint wireless communication and computing resource scheduling method for heterogeneous AI training tasks.Based on Split Federated Learning(SFL),the model split point,wireless bandwidth,and edge computing frequency are integrated into a unified optimization framework.The end-to-end training latency and system energy consumption models are constructed,and task workload profiles together with differentiated service sensitivity coefficients are introduced to characterize the resource requirements of different training tasks.Simulation results show that,compared with the Comm-Only-SFL baseline that optimizes only communication resources,the proposed method reduces the average system cost by 38.89％,while saving training latency and total system energy consumption by 21.75％and 31.98％,respectively.These results verify the effectiveness of the proposed method for heterogeneous AI training tasks.关键词
异构AI训练任务/无线通信/联邦拆分学习/通信计算联合调度/资源优化Key words
heterogeneous AI training tasks/wireless communication/split federated learning/communication-computing joint scheduling/resource optimization分类
信息技术与安全科学引用本文复制引用
陈晶莹,张申虎,闫实..面向异构AI训练任务的无线通信与计算资源联合调度方法[J].移动通信,2026,50(8):56-66,11.基金项目
国家科技重大专项"6G无线接入网智能化关键技术研究、标准推进与验证"(GXB-2-2025-5) （GXB-2-2025-5）
国家自然科学基金项目"面向差异化服务的通信-感知-计算融合无线组网理论与方法"(62371067) （62371067）
