Customized Federated Learning for accelerated edge computing with heterogeneous task targets
文献类型：期刊论文
| 作者 | Jiang, Hui1; Liu, Min1; Yang, Bo1; Liu, Qingxiang1; Li, Jizhong3; Guo, Xiaobing2 |
| 刊名 | COMPUTER NETWORKS
 |
| 出版日期 | 2020-12-24 |
| 卷号 | 183页码:13 |
| ISSN号 | 1389-1286 |
| 关键词 | Edge computing Federated Learning Convergence performance |
| DOI | 10.1016/j.comnet.2020.107569 |
| 英文摘要 | As a dominant edge intelligence technique, Federated Learning (FL) can reduce the data transmission volume, shorten the communication latency and improve the collaboration efficiency among end-devices and edge servers. Existing works on FL-based edge computing only take device- and resource-heterogeneity into consideration under a fixed loss-minimization objective. As heterogeneous end-devices are usually assigned with various tasks with different target accuracies, task heterogeneity is also a significant issue and has not yet been investigated. To this end, we propose a Customized FL (CuFL) algorithm with an adaptive learning rate to tailor for heterogeneous accuracy requirements and to accelerate the local training process. We also present a fair global aggregation strategy for the edge server to minimize the variance of accuracy gaps among heterogeneous end-devices. We rigorously analyze the convergence property of the CuFL algorithm in theory. We also verify the feasibility and effectiveness of the CuFL algorithm in the vehicle classification task. Evaluation results demonstrate that our algorithm performs better in terms of the accuracy rate, training time, and fairness during aggregation than existing efforts. |
| 资助项目 | National Natural Science Foundation of China[61732017] ; National Natural Science Foundation of China[62072436] ; National Natural Science Foundation of China[61872028] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000599651100014 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/16529]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Liu, Min |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China 2.Lenovo Res, Beijing, Peoples R China 3.Huawei Technol Co Ltd, Cent Software Inst, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Hui,Liu, Min,Yang, Bo,et al. Customized Federated Learning for accelerated edge computing with heterogeneous task targets[J]. COMPUTER NETWORKS,2020,183:13. |
| APA | Jiang, Hui,Liu, Min,Yang, Bo,Liu, Qingxiang,Li, Jizhong,&Guo, Xiaobing.(2020).Customized Federated Learning for accelerated edge computing with heterogeneous task targets.COMPUTER NETWORKS,183,13. |
| MLA | Jiang, Hui,et al."Customized Federated Learning for accelerated edge computing with heterogeneous task targets".COMPUTER NETWORKS 183(2020):13. |
入库方式： OAI收割
来源：计算技术研究所
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