Research on the application of government big data platform based on federated learning
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.
government big datafederated learningrecommendation algorithmprivate set intersection
万方数据
维普
