Abstract: A computer-implemented method for training a machine learning model in a distributed system, the distributed system comprising a plurality of nodes that exchange updates to communally train the machine learning model. The method comprises a node: receiving an update to a local model from one or more other nodes in the distributed system, the local model being a locally maintained version of the machine learning model and the update specifying a change to one or more parameters of the local model; updating the local model based on the received update to determine an updated local model; determining for each parameter in the local model a change in the parameter relative to a previous version of the local model; and sending an update to the one or more other nodes in the distributed system, wherein the update includes an update to each parameter that has a change greater than a threshold.

Abstract: A computer-implemented method for training a machine learning model, the method comprising performing, by a computing device, a plurality of training iterations, wherein each training iteration comprises inputting a set of training data to the machine learning model, determining an output of the model from processing the set of training data, and updating one or more parameters of the model based on the output of the model, the method further comprising, for one or more of the training iterations, determining, based on the output of the model for the training iteration, a measure of the stability of the model; and determining, based on the stability of the model, whether to send the updated model parameters via a communication channel to a remote computing device.