Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment
To overcome the three key challenges of federated learning in heterogeneous edge computing, i.e., edge heterogeneity, data Non-IID, and communication resource constraints, a grouping asynchronous federated learning (FedGA) mechanism was proposed.Edge nodes were divided into multiple groups, each of...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023196/ |
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| _version_ | 1841540066279686144 |
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| author | Qianpiao MA Qingmin JIA Jianchun LIU Hongli XU Renchao XIE Tao HUANG |
| author_facet | Qianpiao MA Qingmin JIA Jianchun LIU Hongli XU Renchao XIE Tao HUANG |
| author_sort | Qianpiao MA |
| collection | DOAJ |
| description | To overcome the three key challenges of federated learning in heterogeneous edge computing, i.e., edge heterogeneity, data Non-IID, and communication resource constraints, a grouping asynchronous federated learning (FedGA) mechanism was proposed.Edge nodes were divided into multiple groups, each of which performed global updated asynchronously with the global model, while edge nodes within a group communicate with the parameter server through time-sharing communication.Theoretical analysis established a quantitative relationship between the convergence bound of FedGA and the data distribution among the groups.A time-sharing scheduling magic mirror method (MMM) was proposed to optimize the completion time of a single round of model updating within a group.Based on both the theoretical analysis for FedGA and MMM, an effective grouping algorithm was designed for minimizing the overall training completion time.Experimental results demonstrate that the proposed FedGA and MMM can reduce model training time by 30.1%～87.4% compared to the existing state-of-the-art methods. |
| format | Article |
| id | doaj-art-b802429876d4411383559945c5879e4e |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-b802429876d4411383559945c5879e4e2025-01-14T06:28:13ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-11-0144799359389499Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environmentQianpiao MAQingmin JIAJianchun LIUHongli XURenchao XIETao HUANGTo overcome the three key challenges of federated learning in heterogeneous edge computing, i.e., edge heterogeneity, data Non-IID, and communication resource constraints, a grouping asynchronous federated learning (FedGA) mechanism was proposed.Edge nodes were divided into multiple groups, each of which performed global updated asynchronously with the global model, while edge nodes within a group communicate with the parameter server through time-sharing communication.Theoretical analysis established a quantitative relationship between the convergence bound of FedGA and the data distribution among the groups.A time-sharing scheduling magic mirror method (MMM) was proposed to optimize the completion time of a single round of model updating within a group.Based on both the theoretical analysis for FedGA and MMM, an effective grouping algorithm was designed for minimizing the overall training completion time.Experimental results demonstrate that the proposed FedGA and MMM can reduce model training time by 30.1%～87.4% compared to the existing state-of-the-art methods.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023196/edge computingfederated learningNon-IIDheterogeneityconvergence analysis |
| spellingShingle | Qianpiao MA Qingmin JIA Jianchun LIU Hongli XU Renchao XIE Tao HUANG Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment Tongxin xuebao edge computing federated learning Non-IID heterogeneity convergence analysis |
| title | Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment |
| title_full | Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment |
| title_fullStr | Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment |
| title_full_unstemmed | Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment |
| title_short | Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment |
| title_sort | client grouping and time sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment |
| topic | edge computing federated learning Non-IID heterogeneity convergence analysis |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023196/ |
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