The invention provides a sliding window sampling-based distributed
machine learning training method and
system thereof. The method comprises the steps of initializing parameters of a
machine learning model; obtaining a
data fragment of all data and independently carrying out model training; collecting multiple rounds of historical gradient expiration degree samples, sampling the samples through sliding, calculating a gradient expiration degree context value, adjusting the learning rate and then initiating a gradient update request; asynchronously collecting the multiple gradient expiration degree samples, updating
global model parameters by using the adjusted learning rates and pushing updated parameters; asynchronously obtaining pushed global parameters for updating, and further carrying out next training; checking the model convergence, if the model is not convergent, carrying out model training cycle; and if the model is convergent, obtaining
model parameters. The learning rate of a learning device is controlled by using the expiration gradient, the stability and the convergence effect of distributed training are improved, the training fluctuation caused by the distributed
system is reduced and the robustness of distributed training is improved.