A Clonal Selection Optimization System for Multiparty Secure Computing
The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices an...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/7638394 |
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| _version_ | 1832549667787767808 |
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| author | Minyu Shi Yongting Zhang Huanhuan Wang Junfeng Hu Xiang Wu |
| author_facet | Minyu Shi Yongting Zhang Huanhuan Wang Junfeng Hu Xiang Wu |
| author_sort | Minyu Shi |
| collection | DOAJ |
| description | The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy. |
| format | Article |
| id | doaj-art-933340830c8e414cae0ac9cd06b557b4 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-933340830c8e414cae0ac9cd06b557b42025-02-03T06:10:45ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/76383947638394A Clonal Selection Optimization System for Multiparty Secure ComputingMinyu Shi0Yongting Zhang1Huanhuan Wang2Junfeng Hu3Xiang Wu4School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou 221000, Jiangsu, ChinaSchool of Medical Information and Engineering, Xuzhou Medical University, Xuzhou 221000, Jiangsu, ChinaSchool of Medical Information and Engineering, Xuzhou Medical University, Xuzhou 221000, Jiangsu, ChinaXuzhou Medical University, Xuzhou 221000, Jiangsu, ChinaSchool of Medical Information and Engineering, Xuzhou Medical University, Xuzhou 221000, Jiangsu, ChinaThe innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy.http://dx.doi.org/10.1155/2021/7638394 |
| spellingShingle | Minyu Shi Yongting Zhang Huanhuan Wang Junfeng Hu Xiang Wu A Clonal Selection Optimization System for Multiparty Secure Computing Complexity |
| title | A Clonal Selection Optimization System for Multiparty Secure Computing |
| title_full | A Clonal Selection Optimization System for Multiparty Secure Computing |
| title_fullStr | A Clonal Selection Optimization System for Multiparty Secure Computing |
| title_full_unstemmed | A Clonal Selection Optimization System for Multiparty Secure Computing |
| title_short | A Clonal Selection Optimization System for Multiparty Secure Computing |
| title_sort | clonal selection optimization system for multiparty secure computing |
| url | http://dx.doi.org/10.1155/2021/7638394 |
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