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ShanghaiTech University Knowledge Management System
| Decentralized Federated Learning under Communication Delays | |
| 2022 | |
| 会议录名称 | 2022 IEEE INTERNATIONAL CONFERENCE ON SENSING, COMMUNICATION, AND NETWORKING, SECON WORKSHOPS 2022 |
| ISSN | 2473-0440 |
| 页码 | 37-42 |
| 发表状态 | 已发表 |
| DOI | 10.1109/SECONWorkshops56311.2022.9926391 |
| 摘要 | Federated learning (FL) provides a privacy-preserving approach to training algorithms across multiple edge agents or servers without sharing raw data. Based on dynamic network topologies, we study a fully decentralized FL framework utilizing gradient descent with momentum (MGD) to accelerate the convergence rate. Due to bounded time-varying transmission delays, model updates are asynchronous between different agents, which leads to time-varying information update delay. Extensive experiments are carried out to demonstrate the performance of DMFL over some related algorithms and further analyze the influence of information update delay, network size, and the data distribution on the convergence performance of DMFL. © 2022 IEEE. |
| 关键词 | Machine learning Privacy-preserving techniques Communication delays Decentralised Delay Distributed machine learning Distributed optimization Federated learning Gradient-descent Information updates Momentum gradient descent Privacy preserving |
| 会议名称 | 2022 IEEE International Conference on Sensing, Communication, and Networking, SECON Workshops 2022 |
| 出版地 | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| 会议地点 | Virtual, Online, Sweden |
| 会议日期 | September 23, 2022 |
| URL | 查看原文 |
| 收录类别 | EI ; CPCI-S |
| 语种 | 英语 |
| 资助项目 | National Natural Science Foundation Program of China (NSFC)["U21B2029","U21A20456"] |
| WOS研究方向 | Computer Science ; Remote Sensing ; Telecommunications |
| WOS类目 | Computer Science, Hardware & Architecture ; Computer Science, Information Systems ; Remote Sensing ; Telecommunications |
| WOS记录号 | WOS:000889468500007 |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| EI入藏号 | 20224813165002 |
| EI主题词 | Gradient methods |
| EI分类号 | 716 Telecommunication ; Radar, Radio and Television ; 718 Telephone Systems and Related Technologies ; Line Communications ; 723.2 Data Processing and Image Processing ; 723.4 Artificial Intelligence ; 921.6 Numerical Methods |
| 原始文献类型 | Conference article (CA) |
| 来源库 | IEEE |
| 引用统计 | 正在获取...
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| 文献类型 | 会议论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/251429 |
| 专题 | 信息科学与技术学院_PI研究组_周勇组 |
| 通讯作者 | Lee, Na |
| 作者单位 | 1.Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China 2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 3.Beijing Univ Posts & Telecommun, State Key Lab NST, Beijing 100876, Peoples R China 4.Huazhong Univ Sci & Technol, Key Lab DMET, Wuhan 430074, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lee, Na,Shan, Hangguan,Song, Meiyan,et al. Decentralized Federated Learning under Communication Delays[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2022:37-42. |
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