Decentralized learning of large machine learning (ml) model(s)

Abstract

Implementations described herein are directed to a framework for decentralized learning of large global machine learning (ML) model(s). In various implementations, remote processor(s) of a remote system can identify a global ML model, select client devices to participate in a given round of decentralized learning of the global ML model, and transmit, to each of the client devices, a processed version of the global ML model that is of a reduced transferrable size. Further, client device processor(s) of a client device can receive the processed version of the global ML model, obtain corresponding client data, perform partial model training, based on processing the corresponding client data, for the processed version of the global ML model to generate a corresponding update, and transmit the corresponding update back to the remote system. Moreover, the remote processor(s) can update, based on at least the corresponding update, the global ML model.