1. A method comprising: determining, by a device, that a machine learning model is to be trained by a plurality of devices in a network; identifying, by the device, a set of training devices from among the plurality of devices in the network to train the machine learning model over a sequence of the set, wherein each of the training devices has a local set of training data; determining, by the device, the sequence of the training devices, wherein the sequence is random or based on estimated routing times between the training devices; instructing, by the device, the training devices in the set to sequentially train the machine learning model using the local data sets on the training devices, wherein a training device of the training devices receives model parameters from a first training device in the set, uses the received model parameters with at least a portion of the local set of training data to generate new model parameters, and forwards the new model parameters to a second training device in the sequence; and receiving, by the device, model parameters from the training devices that have been trained using a global set of training data comprising the local sets of training data on the training devices.