计算机工程与应用2024，Vol.60Issue(11)：336-345,10.DOI:10.3778/j.issn.1002-8331.2302-0383
面向联邦学习的无人机轨迹与资源联合优化
Joint Optimization of UAV Trajectory and Resource Allocation for Federal Learning
摘要
Abstract
Unmanned aerial vehicles(UAVs),due to their mobility and flexibility,are widely used in tasks such as search and tracking.The large amount of data generated during task execution can be significantly leveraged by machine learning(ML)algorithms to improve the intelligence of UAV clusters.Federated learning(FL),as a distributed machine learning architecture,is more suitable for UAV networks with limited bandwidth and energy budget,as it only requires model parameter transmission during training.To fully exploit the advantages of FL in UAV networks,this paper models factors affecting training energy consumption,such as bandwidth,computational frequency,FL accuracy,and training delay.A joint optimization algorithm is proposed to minimize the overall training energy consumption of FL by jointly optimizing training parameter settings,UAV trajectories,resource allocation of communication and computation in UAV networks.The proposed joint optimization algorithm decomposes the mixed integer nonlinear integer programming problem(MINLP)into three sub-problems,and transforms the non-convex sub-problems into convex sub-problems through methods such as successive convex approximation(SCA)and block coordinate descent to obtain suboptimal solutions.Simulation results show that,based on the constraints of training accuracy,UAV movement,and FL delay,the proposed algorithm reduces the overall training energy consumption of unmanned aerial vehicles by more than 15%compared to the existing joint training and resource optimization schemes.关键词
联邦学习/无人机群/降低能耗/轨迹优化/资源分配Key words
federal learning/unmanned aerial vehicles(UAVs)/reducing energy consumption/trajectory optimization/resource allocation分类
信息技术与安全科学引用本文复制引用
姚献财,郑建超,郑鑫,杨小龙..面向联邦学习的无人机轨迹与资源联合优化[J].计算机工程与应用,2024,60(11):336-345,10.基金项目
国家自然科学基金(62171454) （62171454）
北京市教育委员会科学研究计划项目(KM202211232006) （KM202211232006）
北京信息科技大学促进高校分类发展-重点研究培育基金(2121YJPY222). （2121YJPY222）
