Distributed machine learning at edge nodes

A system for distributed machine learning at an edge node of a distributed computer system, the system comprising:

• a memory of edge node comprising computer readable instructions;
• and
• a processing device of the edge node for executing the computer readable instructions for performing a method, the method comprising:
• executing a training process of a machine learning model at the edge node for a number of iterations to generate a model parameter based at least in part on a local dataset of the edge node and a global model parameter of the distributed computer system;
• estimating a resource parameter set indicative of a plurality of resources available at the edge node;
• sending the model parameter and the resource parameter set to a synchronization node of the distributed computing system;
• receiving updates to the global model parameter and the number of iterations from the synchronization node based at least in part on the model parameter and the resource parameter set of a plurality of edge nodes of the distributed computer system;
• and
• repeating the training process of the machine learning model to determine an update to the model parameter based at least in part on the local dataset and updates to the global model parameter and the number of iterations from the synchronization node.

A computer program product for distributed machine learning at an edge node of a distributed computer system, the computer program product comprising:

• a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing device to cause the processing device to perform a method comprising:
• executing a training process of a machine learning model at the edge node for a number of iterations to generate a model parameter based at least in part on a local dataset of the edge node and a global model parameter of the distributed computer system;
• estimating, by the edge node, a resource parameter set indicative of a plurality of resources available at the edge node;
• sending the model parameter and the resource parameter set to a synchronization node of the distributed computing system;
• receiving at the edge node updates to the global model parameter and the number of iterations from the synchronization node based at least in part on the model parameter and the resource parameter set of a plurality of edge nodes of the distributed computer system;
• and
• repeating the training process of the machine learning model at the edge node to determine an update to the model parameter based at least in part on the local dataset and updates to the global model parameter and the number of iterations from the synchronization node.