Huber loss based distributed robust learning algorithm for random vector functional-link network
In this paper, we propose two algorithms based on the random vector functional link network (RVFLN) and alternating direction method of multipliers algorithm to solve distributed learning (DL) problems with datasets containing outliers. In distributed scenarios, training datasets are separately divi...
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| Vydané v: | The Artificial intelligence review Ročník 56; číslo 8; s. 8197 - 8218 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Dordrecht
Springer Netherlands
01.08.2023
Springer Springer Nature B.V |
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| ISSN: | 0269-2821, 1573-7462 |
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| Abstract | In this paper, we propose two algorithms based on the random vector functional link network (RVFLN) and alternating direction method of multipliers algorithm to solve distributed learning (DL) problems with datasets containing outliers. In distributed scenarios, training datasets are separately divided and stored on each node of the communication network and cannot be shared or collected over the communication network due to privacy-preserving policy and network environmental restrictions. Thus, each node of the communication network only deals with local data and shares updated output weights of the global RVFLN model with its neighbors. However, the majority of existing DL algorithms are not robust enough when the dataset contain outliers. To overcome the this drawback, we intend to apply the
L
1
norm and Huber based error terms to the global loss function and propose the corresponding distributed robust learning algorithms and denote them as the L1-DRVFL and Huber-DRVFL algorithms, respectively. The proposed algorithms work in fully distributed manner and are privacy-preserving methods. Experiments show that the proposed algorithms are robust and efficient in distributed learning with data including outliers. Moreover, the Huber-DRVFL algorithm is more stable than the L1-DRVFL algorithm when the parameters vary. |
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| AbstractList | In this paper, we propose two algorithms based on the random vector functional link network (RVFLN) and alternating direction method of multipliers algorithm to solve distributed learning (DL) problems with datasets containing outliers. In distributed scenarios, training datasets are separately divided and stored on each node of the communication network and cannot be shared or collected over the communication network due to privacy-preserving policy and network environmental restrictions. Thus, each node of the communication network only deals with local data and shares updated output weights of the global RVFLN model with its neighbors. However, the majority of existing DL algorithms are not robust enough when the dataset contain outliers. To overcome the this drawback, we intend to apply the L1 norm and Huber based error terms to the global loss function and propose the corresponding distributed robust learning algorithms and denote them as the L1-DRVFL and Huber-DRVFL algorithms, respectively. The proposed algorithms work in fully distributed manner and are privacy-preserving methods. Experiments show that the proposed algorithms are robust and efficient in distributed learning with data including outliers. Moreover, the Huber-DRVFL algorithm is more stable than the L1-DRVFL algorithm when the parameters vary. In this paper, we propose two algorithms based on the random vector functional link network (RVFLN) and alternating direction method of multipliers algorithm to solve distributed learning (DL) problems with datasets containing outliers. In distributed scenarios, training datasets are separately divided and stored on each node of the communication network and cannot be shared or collected over the communication network due to privacy-preserving policy and network environmental restrictions. Thus, each node of the communication network only deals with local data and shares updated output weights of the global RVFLN model with its neighbors. However, the majority of existing DL algorithms are not robust enough when the dataset contain outliers. To overcome the this drawback, we intend to apply the L 1 norm and Huber based error terms to the global loss function and propose the corresponding distributed robust learning algorithms and denote them as the L1-DRVFL and Huber-DRVFL algorithms, respectively. The proposed algorithms work in fully distributed manner and are privacy-preserving methods. Experiments show that the proposed algorithms are robust and efficient in distributed learning with data including outliers. Moreover, the Huber-DRVFL algorithm is more stable than the L1-DRVFL algorithm when the parameters vary. In this paper, we propose two algorithms based on the random vector functional link network (RVFLN) and alternating direction method of multipliers algorithm to solve distributed learning (DL) problems with datasets containing outliers. In distributed scenarios, training datasets are separately divided and stored on each node of the communication network and cannot be shared or collected over the communication network due to privacy-preserving policy and network environmental restrictions. Thus, each node of the communication network only deals with local data and shares updated output weights of the global RVFLN model with its neighbors. However, the majority of existing DL algorithms are not robust enough when the dataset contain outliers. To overcome the this drawback, we intend to apply the [Formula omitted] norm and Huber based error terms to the global loss function and propose the corresponding distributed robust learning algorithms and denote them as the L1-DRVFL and Huber-DRVFL algorithms, respectively. The proposed algorithms work in fully distributed manner and are privacy-preserving methods. Experiments show that the proposed algorithms are robust and efficient in distributed learning with data including outliers. Moreover, the Huber-DRVFL algorithm is more stable than the L1-DRVFL algorithm when the parameters vary. |
| Audience | Academic |
| Author | Liu, Sanyang Chen, Jiaxi Jia, Jinping Xie, Jin |
| Author_xml | – sequence: 1 givenname: Jin orcidid: 0000-0001-8007-5683 surname: Xie fullname: Xie, Jin organization: School of Mathematics and Statistics, Xidian University – sequence: 2 givenname: Sanyang surname: Liu fullname: Liu, Sanyang organization: School of Mathematics and Statistics, Xidian University – sequence: 3 givenname: Jiaxi surname: Chen fullname: Chen, Jiaxi email: jxchen208@163.com organization: School of Mathematics and Statistics, Xidian University – sequence: 4 givenname: Jinping surname: Jia fullname: Jia, Jinping organization: School of Mathematics and Statistics, Tianshui Normal University |
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| Keywords | Alternating direction method of multipliers Huber loss function Distributed learning Random vector functional link network Distributed robust learning |
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| SubjectTerms | Algorithms Analysis Artificial Intelligence Communication Communications networks Computational linguistics Computer Science Data mining Datasets Environmental policy Experiments Language processing Learning Machine learning Natural language interfaces Networks Outliers (statistics) Privacy Robustness Vectors (mathematics) |
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| Title | Huber loss based distributed robust learning algorithm for random vector functional-link network |
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