Analysis of heterogeneous data model based on federated learning

Yating Gao; Xingjie Huang; Jinmeng Zhao; Jing Zhang; Xinyu Liu

doi:10.1117/12.2668315

28 March 2023 Analysis of heterogeneous data model based on federated learning

Yating Gao, Xingjie Huang, Jinmeng Zhao, Jing Zhang, Xinyu Liu

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125661B (2023) https://doi.org/10.1117/12.2668315
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

The rapid development of edge network devices has led to the explosive growth of their data, and the difficulty of dealing with heterogeneous data in edge devices has been further increased. To solve the problem of heterogeneous data fusion without interaction, this paper proposes a data heterogeneous model analysis based on federated learning. Preprocess the multi-source heterogeneous data to obtain the main features of the condensed data. Then, the multi-source heterogeneous data nodes are positioned to avoid multi-fusion results, and Spatio-temporal correlation degree of the multi-source heterogeneous data is calculated to improve the accuracy of fusion. Finally, a multi-source heterogeneous data fusion model is established based on federated learning to ensure the security of data fusion. Compared with the traditional model, the data fusion of the proposed model is more stable, and the error is smaller. The effectiveness of the proposed model is verified by the stability and accuracy of the fusion of the heterogeneous data. The multi-source heterogeneous data fusion model studied in this paper can improve the quality of Internet of Things data and promote the development of edge devices in China.

Citation Download Citation

Yating Gao, Xingjie Huang, Jinmeng Zhao, Jing Zhang, and Xinyu Liu "Analysis of heterogeneous data model based on federated learning", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125661B (28 March 2023); https://doi.org/10.1117/12.2668315

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KEYWORDS

Data modeling

Data fusion

Machine learning

Education and training

Feature fusion

Data conversion

Data analysis

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