Network modal innovation for distributed machine learning
Distributed machine learning, as a popular computing architecture for artificial intelligence, still faces challenges of slow model training and poor data performance transmission.Traditional network modalities were un able to meet the communication needs of distributed machine learning scenarios, h...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2023-06-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2023128 |
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| _version_ | 1850213088240861184 |
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| author | Zehua GUO Haowen ZHU Tongwen XU |
| author_facet | Zehua GUO Haowen ZHU Tongwen XU |
| author_sort | Zehua GUO |
| collection | DOAJ |
| description | Distributed machine learning, as a popular computing architecture for artificial intelligence, still faces challenges of slow model training and poor data performance transmission.Traditional network modalities were un able to meet the communication needs of distributed machine learning scenarios, hindering the improvement of model training performance.New network modalities and operation logic for distributed machine learning scenarios using multimodal network technology were proposed.This approach was designed based on application characteristics and provides implications for the use of multimodal network technology in various industries. |
| format | Article |
| id | doaj-art-ccbdae1c4cf54f0282523d7991debd9a |
| institution | OA Journals |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-06-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-ccbdae1c4cf54f0282523d7991debd9a2025-08-20T02:09:12ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-06-0139445159565467Network modal innovation for distributed machine learningZehua GUOHaowen ZHUTongwen XUDistributed machine learning, as a popular computing architecture for artificial intelligence, still faces challenges of slow model training and poor data performance transmission.Traditional network modalities were un able to meet the communication needs of distributed machine learning scenarios, hindering the improvement of model training performance.New network modalities and operation logic for distributed machine learning scenarios using multimodal network technology were proposed.This approach was designed based on application characteristics and provides implications for the use of multimodal network technology in various industries.http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2023128multimodal network;distributed machine learning;model training;artificial intelligence |
| spellingShingle | Zehua GUO Haowen ZHU Tongwen XU Network modal innovation for distributed machine learning Dianxin kexue multimodal network;distributed machine learning;model training;artificial intelligence |
| title | Network modal innovation for distributed machine learning |
| title_full | Network modal innovation for distributed machine learning |
| title_fullStr | Network modal innovation for distributed machine learning |
| title_full_unstemmed | Network modal innovation for distributed machine learning |
| title_short | Network modal innovation for distributed machine learning |
| title_sort | network modal innovation for distributed machine learning |
| topic | multimodal network;distributed machine learning;model training;artificial intelligence |
| url | http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2023128 |
| work_keys_str_mv | AT zehuaguo networkmodalinnovationfordistributedmachinelearning AT haowenzhu networkmodalinnovationfordistributedmachinelearning AT tongwenxu networkmodalinnovationfordistributedmachinelearning |