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Prediction of economic benefits of market digital transformation based on federal learning algorithm

Author

Listed:
  • Lu Zhang
  • Wanqing Chen
  • Hengzhi Nie

Abstract

In order to solve the problems of low prediction accuracy and long prediction time existing in traditional methods for predicting the economic benefits of market digital transformation, a method for predicting the economic benefits of market digital transformation based on federal learning algorithm is proposed. Obtain the prediction indicators and build a prediction indicator system for the economic benefits of market digital transformation. Pre-process the indicator data using the principal component analysis method, build a sample dataset, build a federal optimisation algorithm using the random gradient descent method, establish a minimum loss function, build a prediction model for the economic benefits of market digital transformation, and output the prediction results. The experimental results show that the prediction accuracy of the proposed method can reach more than 95%, and the prediction time is always kept within 3 s, with good prediction effect and efficiency.

Suggested Citation

  • Lu Zhang & Wanqing Chen & Hengzhi Nie, 2023. "Prediction of economic benefits of market digital transformation based on federal learning algorithm," International Journal of Sustainable Development, Inderscience Enterprises Ltd, vol. 26(3/4), pages 190-205.
    • Handle: RePEc:ids:ijsusd:v:26:y:2023:i:3/4:p:190-205
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