User scheduling and power allocation strategy for cell-free networks based on federated learning
In order to address the issue of limited training performance in federated learning (FL) due to user link quality disparities and imbalanced communication, and computing resource utilization in cell-free network systems, a joint optimization problem for user scheduling and power allocation was desig...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-09-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024159/ |
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| author | WANG Huahua HUANG Yexia LI Ling |
| author_facet | WANG Huahua HUANG Yexia LI Ling |
| author_sort | WANG Huahua |
| collection | DOAJ |
| description | In order to address the issue of limited training performance in federated learning (FL) due to user link quality disparities and imbalanced communication, and computing resource utilization in cell-free network systems, a joint optimization problem for user scheduling and power allocation was designed. Firstly, a low-complexity resource priority based secondary sampling user scheduling (RPSS-US) algorithm was proposed. Users were selected based on the availability of their computing resources and link quality, with priority given to those contributing more to system capacity and global model updates, thus improving overall training performance. Then, a power allocation algorithm based on the binary method (BM-PA) was proposed to optimize power allocation, improve user link quality differences, enhance data transmission rates, and reduce overall FL task delay. By iteratively optimizing these two sub-problems alternately, joint optimization of system performance was achieved. Simulation results demonstrate that compared to other comparison algorithms, the proposed algorithm achieves a 47.19% increase in downlink throughput, a 22.60% increase in uplink throughput, and a 57.33% reduction in FL task time consumption, while minimizing time overhead for achieving the same model accuracy |
| format | Article |
| id | doaj-art-7488feed58ff4feeb455d61cbff4f839 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-7488feed58ff4feeb455d61cbff4f8392025-01-14T07:25:03ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-09-014512914373359135User scheduling and power allocation strategy for cell-free networks based on federated learningWANG HuahuaHUANG YexiaLI LingIn order to address the issue of limited training performance in federated learning (FL) due to user link quality disparities and imbalanced communication, and computing resource utilization in cell-free network systems, a joint optimization problem for user scheduling and power allocation was designed. Firstly, a low-complexity resource priority based secondary sampling user scheduling (RPSS-US) algorithm was proposed. Users were selected based on the availability of their computing resources and link quality, with priority given to those contributing more to system capacity and global model updates, thus improving overall training performance. Then, a power allocation algorithm based on the binary method (BM-PA) was proposed to optimize power allocation, improve user link quality differences, enhance data transmission rates, and reduce overall FL task delay. By iteratively optimizing these two sub-problems alternately, joint optimization of system performance was achieved. Simulation results demonstrate that compared to other comparison algorithms, the proposed algorithm achieves a 47.19% increase in downlink throughput, a 22.60% increase in uplink throughput, and a 57.33% reduction in FL task time consumption, while minimizing time overhead for achieving the same model accuracyhttp://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024159/cell-free networkfederated learninguser schedulingpower allocationjoint optimization |
| spellingShingle | WANG Huahua HUANG Yexia LI Ling User scheduling and power allocation strategy for cell-free networks based on federated learning Tongxin xuebao cell-free network federated learning user scheduling power allocation joint optimization |
| title | User scheduling and power allocation strategy for cell-free networks based on federated learning |
| title_full | User scheduling and power allocation strategy for cell-free networks based on federated learning |
| title_fullStr | User scheduling and power allocation strategy for cell-free networks based on federated learning |
| title_full_unstemmed | User scheduling and power allocation strategy for cell-free networks based on federated learning |
| title_short | User scheduling and power allocation strategy for cell-free networks based on federated learning |
| title_sort | user scheduling and power allocation strategy for cell free networks based on federated learning |
| topic | cell-free network federated learning user scheduling power allocation joint optimization |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024159/ |
| work_keys_str_mv | AT wanghuahua userschedulingandpowerallocationstrategyforcellfreenetworksbasedonfederatedlearning AT huangyexia userschedulingandpowerallocationstrategyforcellfreenetworksbasedonfederatedlearning AT liling userschedulingandpowerallocationstrategyforcellfreenetworksbasedonfederatedlearning |