物联网学报2024，Vol.8Issue(4)：70-88,19.DOI:10.11959/j.issn.2096-3750.2024.00438
面向算力物联网的联邦学习系统及设计研究进展
Recent advances on federated learning systems and the design for computing power Internet of things
摘要
Abstract
Computing power Internet of things(CPIoT)integrates Internet of things(IoT)devices with substantial compu-tational resources to support data-intensive tasks,facilitating intelligent decision-making.Within the context of privacy protection requirements for CPIoT,federated learning(FL)that is a distributed learning technique upholds data privacy,and offers a novel approach to addressing data silos for executing complex training tasks,and training large models.Although researchers have been committed to develop more mature federated learning systems to adapt to the CPIoT envi-ronment,current research lacks in-depth exploration of the strengths and limitations,technical features and differences,and support and applicability of federated learning system design techniques.Firstly,the most influential federated learning systems in the industry were studied,including open-source frameworks and benchmarking platforms.The sys-tem design differences in various technical dimensions of CPIoT in an in-depth comparison were analyzed.Detailed crite-ria and recommendations for selecting open-source frameworks and benchmarking platforms in the CPIoT environment were established,so that developers could efficiently choose the most suitable frameworks and platforms.Seeondly,vari-ous experiments for selecting federated learning systems and building complete systems were presented in multiple CPIoT scenarios,to assist developers in better realizing federated learning applications by utilizing the aforementioned technologies.Finally,the current state of standardization and development challenges in the field of federated learning system design were summarized,and future development prospects were discussed.The purpose is to provide a compre-hensive overview of FL systems and the design research progress,serving as a reference for the deep integration of CPIoT and FL networks and offering insights for future research.关键词
算力物联网/联邦学习/开源框架/基准测试平台/计算范例Key words
CPIoT/FL/open-source framework/benchmarking platform/computing paradigm分类
信息技术与安全科学引用本文复制引用
鲁剑锋,祁盼,潘林雨,李冰,曹书琴,靳延安..面向算力物联网的联邦学习系统及设计研究进展[J].物联网学报,2024,8(4):70-88,19.基金项目
国家自然科学基金资助项目(No.62072411,No.62372343,No.62402352) （No.62072411,No.62372343,No.62402352）
浙江省自然科学基金资助项目(No.LR21F020001) （No.LR21F020001）
湖北省重点研发项目(No.2023BEB024) （No.2023BEB024）
武汉东湖新技术开发区"揭榜挂帅"项目(No.2024KJB301)The National Natural Science Foundation of China(No.62072411,No.62372343,No.62402352),The Zhejiang Provincial Natural Science Foundation(No.LR21F020001),The Key Research and Development Program of Hubei Province(No.2023BEB024),The"Revealing the Leader"Project in Wuhan Donghu New Technology Development Zone(No.2024KJB301) （No.2024KJB301）
