公开(公告)号：US20230214642A1

公开(公告)日：2023-07-06

申请号：US17568933

申请日：2022-01-05

Applicant: Google LLC

Inventor： Hakim Sidahmed ,  Zheng Xu ,  Mingqing Chen ,  Yuan Cao ,  Ankush Garg

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Example aspects of the present disclosure provide a novel, resource-efficient approach for federated machine learning techniques with PTNs. The system can determine a first set of training parameters from a plurality of parameters of the global model. Additionally, the system can generate a random seed, using a random number generator, based on a set of frozen parameters. Moreover, the system can transmit, respectively to a plurality of client computing devices, a first set of training parameters and the random seed. Furthermore, the system can receive, respectively from the plurality of client computing devices, updates to one or more parameters in the first set of training parameters. Subsequently, the system can aggregate the updates to one or more parameters that are respectively received from the plurality of client computing devices. The system can modify one or more global parameters of the global model based on the aggregation.