基于联邦学习的政务 大数据平台应用研究.

At present, the construction of digital government has entered a deep water 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. [ABSTRACT FROM AUTHOR]