Machine learning on the edge, distributed machine learning

Distributed machine learning is a technique used when it is not desirable to transmit local data to a central server for the purpose of machine learning. This approach enables local nodes to train models locally and then periodically send the models to the central server. Once there, the models are combined and then redistributed to the local nodes for further use.