电讯技术2025，Vol.65Issue(5)：700-709,10.DOI:10.20079/j.issn.1001-893x.240530002
融合差分隐私联邦学习的无人机辅助边缘计算任务调度
UAV-assisted Edge Computing Task Scheduling with Differential Privacy Federated Learning
摘要
Abstract
As the Internet of Things(IoT)expands,unmanned aerial vehicle(UAV)-assisted edge computing becomes crucial for enhancing network performance and data processing capabilities.However,in dynamic environments,obstacles may cause communication interruptions between UAV and users,indirectly affecting the data processing performance of user devices(UDs),posing challenges to time sensitive data processing and user privacy protection.In order to solve this problem,a task scheduling scheme for UAV-assisted edge computing is proposed,which aims to solve the problem of delay cost optimization and user privacy under complex conditions involving physical obstacles.By coordinating the scheduling of UDs,UAV trajectory,and task offloading rate based on local differential privacy federated learning(LDP-FL)framework,this scheme significantly reduces the average total delay cost of the system while enhancing UDs privacy protection.Each UAV-UDs group is assigned an independent deep reinforcement learning(DRL)agent that develops localized training models.Then,a weighted aggregation algorithm based on LDP-FL is used to process and aggregate their gradients to optimize these models and enhance system delay performance and privacy security.Compared with the existing algorithms integrating federated learning and DRL,the proposed scheme reduces the delay cost by 20.11％,and the delay cost is reduced by 25.46％,19.03％,14.59％and 15.12％respectively in term of the number of UDs,the size of random tasks,the computing power and flight duration of the UAV.关键词
无人机通信/任务调度/边缘计算/联邦学习/差分隐私Key words
UAV communication/task scheduling/edge computing/federated learning/differential privacy分类
电子信息工程引用本文复制引用
刘建华,王可心,涂晓光,樊荣..融合差分隐私联邦学习的无人机辅助边缘计算任务调度[J].电讯技术,2025,65(5):700-709,10.基金项目
国家自然科学基金资助项目(62061003) （62061003）
中国博士后科学基金项目(2022M722248) （2022M722248）
中央高校基本科研业务费专项资金(J2023-027) （J2023-027）
