Online Supervised Learning with Distributed Features over Multiagent System
Most current online distributed machine learning algorithms have been studied in a data-parallel architecture among agents in networks. We study online distributed machine learning from a different perspective, where the features about the same samples are observed by multiple agents that wish to co...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8830359 |
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| _version_ | 1832567727485616128 |
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| author | Xibin An Bing He Chen Hu Bingqi Liu |
| author_facet | Xibin An Bing He Chen Hu Bingqi Liu |
| author_sort | Xibin An |
| collection | DOAJ |
| description | Most current online distributed machine learning algorithms have been studied in a data-parallel architecture among agents in networks. We study online distributed machine learning from a different perspective, where the features about the same samples are observed by multiple agents that wish to collaborate but do not exchange the raw data with each other. We propose a distributed feature online gradient descent algorithm and prove that local solution converges to the global minimizer with a sublinear rate O2T. Our algorithm does not require exchange of the primal data or even the model parameters between agents. Firstly, we design an auxiliary variable, which implies the information of the global features, and estimate at each agent by dynamic consensus method. Then, local parameters are updated by online gradient descent method based on local data stream. Simulations illustrate the performance of the proposed algorithm. |
| format | Article |
| id | doaj-art-5dced9d5f04b4ef4ac6e5b9266d444d3 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5dced9d5f04b4ef4ac6e5b9266d444d32025-02-03T01:00:41ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88303598830359Online Supervised Learning with Distributed Features over Multiagent SystemXibin An0Bing He1Chen Hu2Bingqi Liu3High-Tech Institute of Xi’an, Xi’an 710025, ChinaHigh-Tech Institute of Xi’an, Xi’an 710025, ChinaHigh-Tech Institute of Xi’an, Xi’an 710025, ChinaHigh-Tech Institute of Xi’an, Xi’an 710025, ChinaMost current online distributed machine learning algorithms have been studied in a data-parallel architecture among agents in networks. We study online distributed machine learning from a different perspective, where the features about the same samples are observed by multiple agents that wish to collaborate but do not exchange the raw data with each other. We propose a distributed feature online gradient descent algorithm and prove that local solution converges to the global minimizer with a sublinear rate O2T. Our algorithm does not require exchange of the primal data or even the model parameters between agents. Firstly, we design an auxiliary variable, which implies the information of the global features, and estimate at each agent by dynamic consensus method. Then, local parameters are updated by online gradient descent method based on local data stream. Simulations illustrate the performance of the proposed algorithm.http://dx.doi.org/10.1155/2020/8830359 |
| spellingShingle | Xibin An Bing He Chen Hu Bingqi Liu Online Supervised Learning with Distributed Features over Multiagent System Complexity |
| title | Online Supervised Learning with Distributed Features over Multiagent System |
| title_full | Online Supervised Learning with Distributed Features over Multiagent System |
| title_fullStr | Online Supervised Learning with Distributed Features over Multiagent System |
| title_full_unstemmed | Online Supervised Learning with Distributed Features over Multiagent System |
| title_short | Online Supervised Learning with Distributed Features over Multiagent System |
| title_sort | online supervised learning with distributed features over multiagent system |
| url | http://dx.doi.org/10.1155/2020/8830359 |
| work_keys_str_mv | AT xibinan onlinesupervisedlearningwithdistributedfeaturesovermultiagentsystem AT binghe onlinesupervisedlearningwithdistributedfeaturesovermultiagentsystem AT chenhu onlinesupervisedlearningwithdistributedfeaturesovermultiagentsystem AT bingqiliu onlinesupervisedlearningwithdistributedfeaturesovermultiagentsystem |