Model Optimization Method Based on Vertical Federated Learning

When Vertical Federated Learning is used to classify tasks, a large number of invalid parameters are produced. In view of the above problems, we propose a general method of parameter sharing and gradient compression for both sides of communication, and improve the homomorphic encryption transfer parameters. The experimental results show that the evaluation index of the classification model is greatly improved compared with the traditional longitudinal federated learning logic regression algorithm.