Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing
文献类型：期刊论文
| 作者 | Cui, Yangguang1,3; Cao, Kun2; Zhou, Junlong4,5; Wei, Tongquan1,3 |
| 刊名 | IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
 |
| 出版日期 | 2023-05-01 |
| 卷号 | 42期号:5页码:1518-1531 |
| 关键词 | Training Servers Cloud computing Delays Costs Computational modeling Prototypes Device frequency determination federated learning (FL) high efficiency low cost mobile-edge cloud computing (MECC) user selection |
| ISSN号 | 0278-0070 |
| DOI | 10.1109/TCAD.2022.3205551 |
| 英文摘要 | Federated learning (FL), an emerging distributed machine learning (ML) technique, allows massive embedded devices and a server to work together for training a global ML model without collecting user data on a server. Most existing approaches adopt the traditional centralized FL paradigm with a single server: one is the cloud-centric FL paradigm and the other is the edge-centric FL paradigm. The cloud-centric FL paradigm is able to manage a large-scale FL system across massive user devices with high communication cost, whereas the edge-centric FL paradigm is capable of coordinating a small-scale FL system benefiting from the low communication delay over wireless networks. To fully exploit the advantages of both, in this article, we develop a distinctive hierarchical FL framework for the promising mobile-edge cloud computing (MECC) system, called HELCHFL, to achieve high-efficiency and low-cost hierarchical FL training. In particular, we formulate the corresponding theoretical foundation for our HELCHFL to ensure hierarchical training performance. Furthermore, to address the inherent communication and user heterogeneity issues of FL training, our HELCHFL develops a utility-driven and heterogeneity-aware heuristic user selection strategy to enhance training performance and reduce training delay. Subsequently, by analyzing and utilizing the slack time in FL training, our HELCHFL introduces a device operating frequency determination approach to reduce training energy cost. Experiments demonstrate that our HELCHFL can enhance the highest accuracy by up to 52.93%, gain the training speedup of up to 483.74%, and obtain up to 45.59% training energy savings compared to state-of-the-art baselines. |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000976102300012 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21437]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wei, Tongquan |
| 作者单位 | 1.East China Normal Univ, Shanghai Trusted Ind Internet Software Collaborat, Shanghai 200062, Peoples R China 2.Jinan Univ, Coll Informat Sci & Technol, Guangzhou 510632, Peoples R China 3.East China Normal Univ, Sch Comp Sci & Technol, Shanghai Key Lab Trustworthy Comp, Shanghai 200062, Peoples R China 4.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China 5.Chinese Acad Sci, State Key Lab Comp Architecture, Inst Comp Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cui, Yangguang,Cao, Kun,Zhou, Junlong,et al. Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2023,42(5):1518-1531. |
| APA | Cui, Yangguang,Cao, Kun,Zhou, Junlong,&Wei, Tongquan.(2023).Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,42(5),1518-1531. |
| MLA | Cui, Yangguang,et al."Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 42.5(2023):1518-1531. |
入库方式： OAI收割
来源：计算技术研究所
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