TDMA-based user scheduling policies for federated learning
To improve the communication efficiency in FL (federated learning), for the scenario with heterogeneous edge user's computing capacity and channel state, a class of time division multiple access (TDMA) based user scheduling policies were proposed for FL.The proposed policies aim to minimize the...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021056/ |
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| _version_ | 1841539281013702656 |
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| author | Meixia TAO Dong WANG Rui SUN Naifu ZHANG |
| author_facet | Meixia TAO Dong WANG Rui SUN Naifu ZHANG |
| author_sort | Meixia TAO |
| collection | DOAJ |
| description | To improve the communication efficiency in FL (federated learning), for the scenario with heterogeneous edge user's computing capacity and channel state, a class of time division multiple access (TDMA) based user scheduling policies were proposed for FL.The proposed policies aim to minimize the system delay in each round of model training subject to a given sample size constraint required for computing in each round.In addition, the convergence rate of the proposed scheduling algorithms was analyzed from a theoretical perspective to study the tradeoff between the convergence performance and the total system delay.The selection of the optimal batch size was further analyzed.Simulation results show that the convergence rate of the proposed algorithm is at least 30% higher than all the considered benchmarks. |
| format | Article |
| id | doaj-art-70d3ed1dfc314f0f96337f0f1177d9ee |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-70d3ed1dfc314f0f96337f0f1177d9ee2025-01-14T07:22:04ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-06-014212959741803TDMA-based user scheduling policies for federated learningMeixia TAODong WANGRui SUNNaifu ZHANGTo improve the communication efficiency in FL (federated learning), for the scenario with heterogeneous edge user's computing capacity and channel state, a class of time division multiple access (TDMA) based user scheduling policies were proposed for FL.The proposed policies aim to minimize the system delay in each round of model training subject to a given sample size constraint required for computing in each round.In addition, the convergence rate of the proposed scheduling algorithms was analyzed from a theoretical perspective to study the tradeoff between the convergence performance and the total system delay.The selection of the optimal batch size was further analyzed.Simulation results show that the convergence rate of the proposed algorithm is at least 30% higher than all the considered benchmarks.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021056/federated learningknapsack problemuser schedulingconvergence analysisedge intelligence |
| spellingShingle | Meixia TAO Dong WANG Rui SUN Naifu ZHANG TDMA-based user scheduling policies for federated learning Tongxin xuebao federated learning knapsack problem user scheduling convergence analysis edge intelligence |
| title | TDMA-based user scheduling policies for federated learning |
| title_full | TDMA-based user scheduling policies for federated learning |
| title_fullStr | TDMA-based user scheduling policies for federated learning |
| title_full_unstemmed | TDMA-based user scheduling policies for federated learning |
| title_short | TDMA-based user scheduling policies for federated learning |
| title_sort | tdma based user scheduling policies for federated learning |
| topic | federated learning knapsack problem user scheduling convergence analysis edge intelligence |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021056/ |
| work_keys_str_mv | AT meixiatao tdmabaseduserschedulingpoliciesforfederatedlearning AT dongwang tdmabaseduserschedulingpoliciesforfederatedlearning AT ruisun tdmabaseduserschedulingpoliciesforfederatedlearning AT naifuzhang tdmabaseduserschedulingpoliciesforfederatedlearning |