摘要
随着物联网设备迅猛激增,其收集的数据上传至云计算中心会造成网络延迟、计算资源浪费等问题.为了解决上述问题,边缘计算作为新的计算范式应运而生,但也面临用户隐私、数据安全等诸多挑战.联邦学习作为时下热点人工智能技术,可以解决隐私数据以及"数据孤岛"问题,将联邦学习应用在边缘计算领域能够有效地处理隐私数据等难题.通过大量调研,本文介绍了边缘计算场景中的联邦学习技术和训练模型,对比分析了联邦学习在边缘聚合、边缘缓存和计算卸载中的应用方案,指明现有方案存在的问题并提出解决思路,探讨了联邦学习在边缘计算中应用的未来研究方向和挑战.
A significant number of Internet of Things( IoT) devices upload their data to the cloud,which lead to some serious problem such as network delay and waste of resource. Edge computing,as a new computation paradigm,can solve the above problems.However,edge computing also has some severe problems to been solved,such as user privacy and data security. Federated learning is a new and hot technology of artificial intelligence that can solve the problem of privacy data and data silos. Applying federated learning to the scenarios of edge computing can solve the problem of user privacy. After lots of research,in this paper,the overview of federated learning and federated learning training model based on edge computing is introduced. Then,we compare methods of federated learning application on edge aggregation,edge cache,and computation offloading,point out the problems of the above existing methods,and give the idea to solve the problems. Finally,we give some future researches directions and challenges of federated learning applications on edge computing.
作者
张依琳
陈宇翔
田晖
王田
ZHANG Yi-lin;CHEN Yu-xiang;TIAN Hui;WANG Tian(College of Computer Science and Technology,Huaqiao University,Xiamen 361021,China;Xiamen Ke Laboratory of Data Security and Blockchain Technology,Huaqiao University,Xiamen 361021,China;Institute of Artificial Intelligence and Future Networks,Beijing Normal University,Zhuhai 519000,China;Guangdong Key Lab of AI and Multi-Modal Data Processing,BNU-HKBU United International College,Zhuhai 519000,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2021年第12期2645-2653,共9页
Journal of Chinese Computer Systems
基金
福建省自然科学基金项目(2020J06023)资助
国家自然科学基金项目(62172046)资助
广东省教育厅普通高校重点领域专项项目(2021ZDZX1063)资助。
关键词
联邦学习
边缘计算
边缘缓存
计算卸载
物联网
federated learning
edge computing
edge cache
computation offloading
internet of things
