Title: Digital ideological and political education data sharing algorithm based on federal incremental learning

Authors: Aihua Mo

Addresses: School of Management, Hunan City University, Hunan, 413000, China

Abstract: Traditional sharing methods have problems such as low safety factor, low recall rate, and low efficiency in data sharing. A federated incremental learning based data sharing algorithm for digital ideological and political education is proposed. Based on the data extraction standards of digital ideological and political education, determine the data centre of digital ideological and political education, and determine the dissimilarity of data through Minkowski distance; Using a random mapping method to convert the data into a consistent pattern, and implementing data preprocessing according to the optimal criteria for classification; calculate the aggregation weight of data, enable shared data to be back-propagated and iteratively updated, combine federated learning and incremental learning, build multiple data sharing clients, and add multiple data security keys to achieve secure sharing of digital ideological and political education data. The test results show that the method has good data sharing security and high sharing efficiency.

Keywords: federal incremental learning; digital ideological and political teaching; data sharing; Minkowski distance; decision tree.

DOI: 10.1504/IJRIS.2025.146932

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.2, pp.146 - 153

Received: 22 Mar 2023
Accepted: 15 May 2023
Published online: 27 Jun 2025 *

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