Abstract: A method for communicating a plurality of numerical parameter updates of a machine learning model from a first node to a second node. The method includes dividing each of the parameter updates into a respective primary segment and one or more respective additional segments, wherein the primary segment of each parameter update is the segment that has the greatest influence on the value of that parameter update. The method further includes constructing primary packet containing the primary segments of each of the plurality of parameter updates, and one or more additional packets including the one or more additional segments of the plurality of parameter updates. The method further includes transmitting the plurality of packets from the first node, wherein the primary packet is transmitted with a higher priority than any of the one or more additional packets.

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.

Abstract: A computer-implemented method for identifying and rectifying a machine learning drift in a federated learning deployment comprising a parameter server and a plurality of worker nodes, wherein a first worker node comprises: a first machine learning model trained using a first data source; and a second machine learning model trained using a second data source; wherein the first data source is generated by the first worker node and the second data source is generated by a second worker node; the method comprising calculating, by the first worker node, using a trusted data set, a first performance metric associated with the first machine learning model and a second performance metric associated with the second machine learning model and determining, by the first worker node, whether a potential drift has occurred in at least one of the first and the second machine learning models.

Abstract: Moving Mobile Wireless Vehicle Network Infrastructure System and Method A system and method for managing a dynamic wireless network comprising at least one movable wireless access point configured to be carried by a vehicle. The method comprises monitoring at least part of the network and determining one or more locations of demand for a wireless access point and receiving location data indicating the current location of the movable wireless access point. The method further comprises determining a route for the vehicle, the route being from the current location of the movable wireless access point and towards one of the one or more locations of demand so that the movable wireless access point may provide wireless network coverage to the location of demand for at least part of the route.