Lightweight decentralized learning-based automatic modulation classification method
In order to solve the problems in centralized learning, a lightweight decentralized learning-based AMC method was proposed.By the proposed decentralized learning, a global model was trained through local training and model weight sharing, which made full use of the dataset of each communication node...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2022-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022145/ |
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| author | Jie YANG Biao DONG Xue FU Yu WANG Guan GUI |
| author_facet | Jie YANG Biao DONG Xue FU Yu WANG Guan GUI |
| author_sort | Jie YANG |
| collection | DOAJ |
| description | In order to solve the problems in centralized learning, a lightweight decentralized learning-based AMC method was proposed.By the proposed decentralized learning, a global model was trained through local training and model weight sharing, which made full use of the dataset of each communication nodes and avoided the user data leakage.The proposed lightweight network was stacked by a number of different lightweight neural network blocks with a relatively low space complexity and time complexity, and achieved a higher recognition accuracy compared with traditional DL models, which could effectively solve the problems of computing power and storage space limitation of edge devices and high communication overhead in decentralized learning based AMC method.The experimental results show that the classification accuracy of the proposed method is 62.41% based on RadioML.2016.10 A.Compared with centralized learning, the training efficiency is nearly 5 times higher with a slight classification accuracy loss (0.68%).In addition, the experimental results also prove that the deployment of lightweight models can effectively reduce communication overhead in decentralized learning. |
| format | Article |
| id | doaj-art-5e67df51514949b49ed2dddfc1f2a4ed |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-5e67df51514949b49ed2dddfc1f2a4ed2025-01-14T06:29:44ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-07-014313414259395071Lightweight decentralized learning-based automatic modulation classification methodJie YANGBiao DONGXue FUYu WANGGuan GUIIn order to solve the problems in centralized learning, a lightweight decentralized learning-based AMC method was proposed.By the proposed decentralized learning, a global model was trained through local training and model weight sharing, which made full use of the dataset of each communication nodes and avoided the user data leakage.The proposed lightweight network was stacked by a number of different lightweight neural network blocks with a relatively low space complexity and time complexity, and achieved a higher recognition accuracy compared with traditional DL models, which could effectively solve the problems of computing power and storage space limitation of edge devices and high communication overhead in decentralized learning based AMC method.The experimental results show that the classification accuracy of the proposed method is 62.41% based on RadioML.2016.10 A.Compared with centralized learning, the training efficiency is nearly 5 times higher with a slight classification accuracy loss (0.68%).In addition, the experimental results also prove that the deployment of lightweight models can effectively reduce communication overhead in decentralized learning.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022145/automatic modulation classificationdecentralized learninglightweight networkdeep learning |
| spellingShingle | Jie YANG Biao DONG Xue FU Yu WANG Guan GUI Lightweight decentralized learning-based automatic modulation classification method Tongxin xuebao automatic modulation classification decentralized learning lightweight network deep learning |
| title | Lightweight decentralized learning-based automatic modulation classification method |
| title_full | Lightweight decentralized learning-based automatic modulation classification method |
| title_fullStr | Lightweight decentralized learning-based automatic modulation classification method |
| title_full_unstemmed | Lightweight decentralized learning-based automatic modulation classification method |
| title_short | Lightweight decentralized learning-based automatic modulation classification method |
| title_sort | lightweight decentralized learning based automatic modulation classification method |
| topic | automatic modulation classification decentralized learning lightweight network deep learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022145/ |
| work_keys_str_mv | AT jieyang lightweightdecentralizedlearningbasedautomaticmodulationclassificationmethod AT biaodong lightweightdecentralizedlearningbasedautomaticmodulationclassificationmethod AT xuefu lightweightdecentralizedlearningbasedautomaticmodulationclassificationmethod AT yuwang lightweightdecentralizedlearningbasedautomaticmodulationclassificationmethod AT guangui lightweightdecentralizedlearningbasedautomaticmodulationclassificationmethod |