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

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.