移动通信2024，Vol.48Issue(3)：75-82,8.DOI:10.3969/j.issn.1006-1010.20240207-0001
面向空中联邦学习的边缘智能感知模型优化方法研究
Sensing Models Optimization in Edge Intelligence for Aerial Federated Learning
摘要
Abstract
Integrated sensing and communication,empowered by artificial intelligence,endows 6G networks with high-precision sensing capabilities of the physical world.To efficiently process the massive sensing data collected at edge nodes and reduce model training delay,aerial federated learning has emerged.This method leverages the superposition property of wireless channels to synchronize model parameter updates across multiple nodes.However,the specific impact of wireless channel noise on model performance in aerial federated learning necessitates in-depth investigation.This study aims to optimize model generalization capabilities,incorporating delay and energy consumption constraints to develop a joint optimization framework for sensing and communication resources.The transmission power allocation problem is addressed using a grid search method,exploring the impact of data batch size and channel noise on model generalization capabilities.Simulation experiments demonstrate that the proposed aerial federated learning strategy effectively enhances the model's generalization performance.关键词
6G无线技术/通信感知一体化/空中联邦学习Key words
6G wireless technology/integrated sensing and communication/aerial federated learning分类
信息技术与安全科学引用本文复制引用
李阳,王新宁,韩凯峰,蔡智捷,朱光旭,徐明枫..面向空中联邦学习的边缘智能感知模型优化方法研究[J].移动通信,2024,48(3):75-82,8.基金项目
第八届"中国科协人才托举工程"项目(2022QNRC001) （2022QNRC001）
国家重点研发项目"6G通信-感知-融合网络架构及关键技术"(2021YFB2900200) （2021YFB2900200）
