Credit risk prediction for small and micro enterprises based on federated transfer learning frozen network parameters

Abstract

To accelerate the convergence speed and improve the accuracy of the federated shared model, this paper proposes a Federated Transfer Learning method based on frozen network parameters. The article sets up frozen two, three, and four layers network parameters, 8 sets of experimental tasks, and two target users for comparative experiments on frozen network parameters, and uses homomorphic encryption based Federated Transfer Learning to achieve secret transfer of parameters, and the accuracy, convergence speed, and loss function values of the experiment were compared and analyzed. The experiment proved that the frozen three-layer network parameter model has the highest accuracy, with the average values of the two target users being 0.9165 and 0.9164; The convergence speed is also the most ideal, with fast convergence completed after 25 iterations. The training time for the two users is also the shortest, with 1732.0s and 1787.3s, respectively; The loss function value shows that the lowest value for User-II is 0.181, while User-III is 0.2061. Finally, the unlabeled and non-empty enterprise credit data is predicted, with 61.08% of users being low-risk users. This article achieves rapid convergence of the target network model by freezing source domain network parameters in a shared network, saving computational resources.