Impact Analysis of Delayed Updates for Mobile Decentralized Federated Learning: A Delay-Tolerant Training Strategy for Real-World Edge Intelligence
文献类型：期刊论文
| 作者 | Zeng, Yong2; Liu, Siyuan1; Xu, Zhiwei3,4; Tian, Jie5 |
| 刊名 | JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
 |
| 出版日期 | 2025-05-01 |
| 卷号 | 41期号:3页码:627-644 |
| 关键词 | edge intelligence decentralized federated learning theoretical bound for delayed updates delay-tolerant decentralized training |
| ISSN号 | 1016-2364 |
| DOI | 10.6688/JISE.20250541(3).0007 |
| 英文摘要 | Decentralized Federated learning is a distributed learning framework by learning a model with aggregated parameters among nearby participants, while keeping all the training data on the participants. Considering the various heterogeneous scenarios of mobile participants, the impact of transmission delay of updates during model training is non-negligible for data-intensive intelligent applications on mobile devices, e.g., intelligent medical services, automated driving vehicles, etc.. Although the latest solutions reuse the parameter updates for model parameter aggregating and approach a global model, there is no rational threshold for the delayed updates to guarantee model convergence. To address this problem, we analyze the impact of delayed updates for decentralized federated learning, and provide a theoretical bound for these updates based on augmented Lagrange function to achieve model convergence. Thereafter, we propose a novel decentralized federated learning scheme to enhance the corporate strategy of mobile computing devices. It releases the requirement for aggregating participants' updates within a specific time period, and provide a theoretical threshold for parameter updating delay. The latest versions for the delayed updates are reused to continue model training, in case the model parameters are not collected or updated within the theoretical threshold. Finally, we implement experiments on a real-world test bed, and demonstrate that delay-adaptive-DFL is more efficient than the latest baselines. |
| 资助项目 | State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)[SKLNST-2020-1-18] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001488545700007 |
| 出版者 | INST INFORMATION SCIENCE |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42385]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Xu, Zhiwei |
| 作者单位 | 1.Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot 010021, Mongolia, Peoples R China 2.Sichuan Tengden Technol Co Ltd, Chengdu 611730, Sichuan, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100864, Peoples R China 4.Tianjin Univ Sci & Technol, Haihe Lab ITAI, Tianjin, Peoples R China 5.New Jersey Inst Technol, Dept Comp Sci, Newark, NJ 07102 USA |
| 推荐引用方式 GB/T 7714 | Zeng, Yong,Liu, Siyuan,Xu, Zhiwei,et al. Impact Analysis of Delayed Updates for Mobile Decentralized Federated Learning: A Delay-Tolerant Training Strategy for Real-World Edge Intelligence[J]. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING,2025,41(3):627-644. |
| APA | Zeng, Yong,Liu, Siyuan,Xu, Zhiwei,&Tian, Jie.(2025).Impact Analysis of Delayed Updates for Mobile Decentralized Federated Learning: A Delay-Tolerant Training Strategy for Real-World Edge Intelligence.JOURNAL OF INFORMATION SCIENCE AND ENGINEERING,41(3),627-644. |
| MLA | Zeng, Yong,et al."Impact Analysis of Delayed Updates for Mobile Decentralized Federated Learning: A Delay-Tolerant Training Strategy for Real-World Edge Intelligence".JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 41.3(2025):627-644. |
入库方式： OAI收割
来源：计算技术研究所
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