基于联邦学习的政务大数据平台应用研究

doi:10.11959/j.issn.2096-0271.2024032

Abstract

Abstract:

At present, the construction of digital government has entered a deepwater area.The government big data platform, as a data base, supports various government information applications.The security and compliance of its private data has been widely concerned by the industry.Federated learning is an important method to effectively solve data silos, and the application of government big data platforms based on federated learning has high research value.Firstly, the current status of government big data platforms and its federated learning application were introduced.Then this paper analyzed three major management challenges involved in the collection, classification and grading and sharing of privacy data on government big data platforms.Further, the problem-solving methods of federated learning based recommendation algorithms and privacy intersection techniques were explored.Finally, summaries and prospects were made for the future application of privacy data on government big data platforms.

Key words: government big data, federated learning, recommendation algorithm, private set intersection

CLC Number:

TP181

Jianping WU, Chaochao CHEN, Jiahe JIN, Chunming WU. Research on the application of government big data platform based on federated learning[J]. Big Data Research, 2024, 10(3): 40-54.

Figures/Tables 6

References 37

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序号	类型	平台上线量	平台上线率
1	省级政务大数据平台	26	76.47%
2	市级政务大数据平台	257	77.18%
3	县级政务大数据平台	355	12.49%

Research on the application of government big data platform based on federated learning

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