Training efficiency optimization algorithm of wireless federated learning based on processor performance and network condition awareness
Abstract With the explosive growth of smart mobile devices in wireless networks, the increasing computational power of mobile chips and the growing concern for personal privacy, a decentralized deep learning framework at the mobile terminal layer has emerged called federated learning (FL) to enhance...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-11-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
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| Online Access: | https://doi.org/10.1186/s13634-024-01192-6 |
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| _version_ | 1850064367270232064 |
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| author | Guohao Pang Xiaorong Zhu |
| author_facet | Guohao Pang Xiaorong Zhu |
| author_sort | Guohao Pang |
| collection | DOAJ |
| description | Abstract With the explosive growth of smart mobile devices in wireless networks, the increasing computational power of mobile chips and the growing concern for personal privacy, a decentralized deep learning framework at the mobile terminal layer has emerged called federated learning (FL) to enhance user experience. This paper studies the training efficiency optimization problem of wireless FL that jointly considers processor performance, channel conditions and terminals’ power in a non-independent identically distribution (non-IID) scenario. And, the training efficiency optimization problem is mathematically modeled and then decomposed into several sub-problems based on the independence and decoupling of the variables involved. To enhance the training efficiency of wireless FL, a comprehensive scheduling strategy encompassing computational and communication aspects is proposed. Simulation results show that the proposed scheduling strategy for wireless FL achieves superior learning performance with reduced training latency. |
| format | Article |
| id | doaj-art-507a1398a0e149348ff06d2f71e0669d |
| institution | DOAJ |
| issn | 1687-6180 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj-art-507a1398a0e149348ff06d2f71e0669d2025-08-20T02:49:19ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802024-11-012024112810.1186/s13634-024-01192-6Training efficiency optimization algorithm of wireless federated learning based on processor performance and network condition awarenessGuohao Pang0Xiaorong Zhu1The College of Portland, Nanjing University of Posts and TelecommunicationsThe College of Telecommunications and Information Engineering, Nanjing University of Posts and TelecommunicationsAbstract With the explosive growth of smart mobile devices in wireless networks, the increasing computational power of mobile chips and the growing concern for personal privacy, a decentralized deep learning framework at the mobile terminal layer has emerged called federated learning (FL) to enhance user experience. This paper studies the training efficiency optimization problem of wireless FL that jointly considers processor performance, channel conditions and terminals’ power in a non-independent identically distribution (non-IID) scenario. And, the training efficiency optimization problem is mathematically modeled and then decomposed into several sub-problems based on the independence and decoupling of the variables involved. To enhance the training efficiency of wireless FL, a comprehensive scheduling strategy encompassing computational and communication aspects is proposed. Simulation results show that the proposed scheduling strategy for wireless FL achieves superior learning performance with reduced training latency.https://doi.org/10.1186/s13634-024-01192-6Federated learningWireless communicationScheduling policiesParallel and distributed algorithmsResource allocation |
| spellingShingle | Guohao Pang Xiaorong Zhu Training efficiency optimization algorithm of wireless federated learning based on processor performance and network condition awareness EURASIP Journal on Advances in Signal Processing Federated learning Wireless communication Scheduling policies Parallel and distributed algorithms Resource allocation |
| title | Training efficiency optimization algorithm of wireless federated learning based on processor performance and network condition awareness |
| title_full | Training efficiency optimization algorithm of wireless federated learning based on processor performance and network condition awareness |
| title_fullStr | Training efficiency optimization algorithm of wireless federated learning based on processor performance and network condition awareness |
| title_full_unstemmed | Training efficiency optimization algorithm of wireless federated learning based on processor performance and network condition awareness |
| title_short | Training efficiency optimization algorithm of wireless federated learning based on processor performance and network condition awareness |
| title_sort | training efficiency optimization algorithm of wireless federated learning based on processor performance and network condition awareness |
| topic | Federated learning Wireless communication Scheduling policies Parallel and distributed algorithms Resource allocation |
| url | https://doi.org/10.1186/s13634-024-01192-6 |
| work_keys_str_mv | AT guohaopang trainingefficiencyoptimizationalgorithmofwirelessfederatedlearningbasedonprocessorperformanceandnetworkconditionawareness AT xiaorongzhu trainingefficiencyoptimizationalgorithmofwirelessfederatedlearningbasedonprocessorperformanceandnetworkconditionawareness |