| 摘要: |
| 为了提高无线联邦学习网络性能，提出了一种用户选择和资源分配的联合优化算法。针对无线联邦学习网络中存在的资源受限和资源异质的问题，研究了长期能量约束下无线联邦学习网络的用户选择、通信资源分配和计算资源分配的联合优化问题，以达到最大化学习效率的目的。基于李雅普诺夫理论，将构建的长期优化问题转化为一系列的短期优化问题，并使用迭代算法优化用户选择和资源分配。与基准算法相比，所提算法能使学习效率提升10%以上，能在满足能量约束的条件下提高测试精度。 |
| 关键词: 无线网络 用户选择 联邦学习 资源分配 学习效率 |
| DOI：10.20079/j.issn.1001-893x.230813002 |
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| 基金项目:国家自然科学基金青年基金项目（61801433） |
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| Joint Optimization of Client Selection and Resource Allocation for Wireless Federated Learning Networks |
| ZHOU Zhaohui,LI Yitong,SUN Zhaozhan |
| (School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China) |
| Abstract: |
| In order to improve the performance of wireless federated learning networks,a joint optimization algorithm for client selection and resource allocation is proposed.Focusing on the resource-constrained and resource-heterogeneous problems in wireless federated learning networks,the joint optimization of client selection,communication resource allocation,and computing resource allocation for wireless federated learning networks is studied under the long-term energy constraints,in order to maximize learning efficiency.Based on Lyapunov theory,the long-term optimization problem is transformed into a series of short-term optimization problems,and an iterative algorithm is employed to optimize client selection and resource allocation.Compared with benchmark algorithms,the proposed algorithm can improve the learning efficiency by more than 10% and increase test accuracy while satisfying energy constraints. |
| Key words: wireless networks federated learning client selection resource allocation learning efficiency |
