Privacy federation learning framework based on principal component analysis

Federal learning is an effective distributed learning technology that allows machine learning model training while protecting data privacy. However, with the increase of the number of user -side devices, the calculation burden of users in federal learning will increase. Researchers explore the use of dimension reduction technology to reduce the calculation burden required for model training, but this triggers a problem with low accuracy. This article extracts the dimensions of gradient data by improving the main component analysis method to extract the dimensions of gradient data and reduce communication and calculation burden while protecting the privacy of the client. The experimental results of this article show that under large -scale data sets, the method of this article increases the speed of 50%training and reaches 96% accuracy.