Abstract: A method, apparatus, computer device and storage medium for distributed training of a machine learning model. The method includes: obtaining more than one group of training samples, and processing each group of training samples separately according to a current training parameter of a machine learning model to obtain a corresponding parameter gradient; determining a local gradient of a current training process based on the parameter gradient corresponding to each group of training samples; transmitting the local gradient of the current training process to a parameter node; and obtaining the updated training parameter from the parameter node, and performing model training on the machine learning model based on the updated training parameter until a training end condition is met.