Adaptively synchronizing learning of multiple learning models

A method, comprising:

• periodically receiving, at a master node of a plurality of data processing nodes, a set of learning model parameters from each node of the plurality of data processing nodes,
• wherein the each successive set of learning model parameters from the respective nodes are iteratively received after a predefined synchronization interval, and wherein the learning model parameters are received for a predefined number of iterations;
• aggregating, after each of the predefined number of iterations, the set of learning model parameters received after the predefined synchronization interval;
• generating central learning models based on the aggregated set of learning model parameters received after each of the predefined number of iterations;
• determining accuracies of the central learning models;
• determining an average accuracy by averaging the accuracies of the central learning models;
• determining a difference between an accuracy of an immediate central learning model and the average accuracy;
• comparing the difference in accuracy to a set of thresholds;
• when the difference in accuracy is outside of a range established by the set of thresholds, modifying the predefined synchronization interval;
• and
• transmitting, to the plurality of data processing nodes, the modified synchronization interval.

A non-transitory machine-readable storage medium that stores instructions which, when executed by a processor, perform a plurality of operations comprising:

• receiving a set of learning model parameters iteratively from each node of a plurality of data processing nodes,
• wherein the set of learning model parameters is received after a predefined synchronization interval, and wherein the learning model parameters are received for a predefined number of iterations;
• generating central learning models based on the set of learning model parameters received after each of the predefined number of iterations;
• determining accuracies of the central learning models;
• determining an average accuracy by averaging the accuracies of the central learning models;
• determining a difference between an accuracy of an immediate central learning model and the average accuracy;
• comparing the difference in accuracy to a range established by a set of thresholds;
• when the difference in accuracy is outside of the range, modifying the predefined synchronization interval;
• and
• transmitting, to the plurality of data processing nodes, the modified synchronization interval.