ADAPTIVELY SYNCHRONIZING LEARNING OF MULTIPLE LEARNING MODELS

摘要

A system and a method for adaptively synchronizing learning of multiple learning models are disclosed. Several local learning models are executed on multiple nodes. Learning model parameters are shared by such nodes to a master node, in multiple iterations, after a predefined synchronization interval. Such learning model parameters are aggregated and central learning models are generated based on aggregated set of learning model parameters. Accuracies of the central learning models and an average accuracy of the central learning models are determined. Accuracy of an immediate central learning model i.e. the one received after determining the average accuracy, is compared with the average accuracy. Based on the difference between the accuracy of the immediate central learning model and the average accuracy, the synchronization interval is modified, and the multiple nodes are updated about this modified synchronization interval.