Communication-efficient federated learning method via redundant data elimination
To address the influence of limited network bandwidth of edge devices on the communication efficiency of federated learning, and efficiently transmit local model update to complete model aggregation, a communication-efficient federated learning method via redundant data elimination was proposed.The...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-05-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023072/ |
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| _version_ | 1841539170550415360 |
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| author | Kaiju LI Qiang XU Hao WANG |
| author_facet | Kaiju LI Qiang XU Hao WANG |
| author_sort | Kaiju LI |
| collection | DOAJ |
| description | To address the influence of limited network bandwidth of edge devices on the communication efficiency of federated learning, and efficiently transmit local model update to complete model aggregation, a communication-efficient federated learning method via redundant data elimination was proposed.The essential reasons for generation of redundant update parameters and according to non-IID properties and model distributed training features of FL were analyzed, a novel sensitivity and loss function tolerance definitions for coreset was given, and a novel federated coreset construction algorithm was proposed.Furthermore, to fit the extracted coreset, a novel distributed adaptive sparse network model evolution mechanism was designed to dynamically adjust the structure and the training model size before each global training iteration, which reduced the number of communication bits between edge devices and the server while also guarantees the training model accuracy.Experimental results show that the proposed method achieves 17% reduction in communication bits transmission while only 0.5% degradation in model accuracy compared with state-of-the-art method. |
| format | Article |
| id | doaj-art-11a2cb1e0aea42a894b705b4727ffab0 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-05-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-11a2cb1e0aea42a894b705b4727ffab02025-01-14T07:23:52ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-05-0144799359838272Communication-efficient federated learning method via redundant data eliminationKaiju LIQiang XUHao WANGTo address the influence of limited network bandwidth of edge devices on the communication efficiency of federated learning, and efficiently transmit local model update to complete model aggregation, a communication-efficient federated learning method via redundant data elimination was proposed.The essential reasons for generation of redundant update parameters and according to non-IID properties and model distributed training features of FL were analyzed, a novel sensitivity and loss function tolerance definitions for coreset was given, and a novel federated coreset construction algorithm was proposed.Furthermore, to fit the extracted coreset, a novel distributed adaptive sparse network model evolution mechanism was designed to dynamically adjust the structure and the training model size before each global training iteration, which reduced the number of communication bits between edge devices and the server while also guarantees the training model accuracy.Experimental results show that the proposed method achieves 17% reduction in communication bits transmission while only 0.5% degradation in model accuracy compared with state-of-the-art method.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023072/federated learningcommunication efficiencycoresetmodel evolutionaccuracy |
| spellingShingle | Kaiju LI Qiang XU Hao WANG Communication-efficient federated learning method via redundant data elimination Tongxin xuebao federated learning communication efficiency coreset model evolution accuracy |
| title | Communication-efficient federated learning method via redundant data elimination |
| title_full | Communication-efficient federated learning method via redundant data elimination |
| title_fullStr | Communication-efficient federated learning method via redundant data elimination |
| title_full_unstemmed | Communication-efficient federated learning method via redundant data elimination |
| title_short | Communication-efficient federated learning method via redundant data elimination |
| title_sort | communication efficient federated learning method via redundant data elimination |
| topic | federated learning communication efficiency coreset model evolution accuracy |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023072/ |
| work_keys_str_mv | AT kaijuli communicationefficientfederatedlearningmethodviaredundantdataelimination AT qiangxu communicationefficientfederatedlearningmethodviaredundantdataelimination AT haowang communicationefficientfederatedlearningmethodviaredundantdataelimination |