公开(公告)号：US20230083116A1

公开(公告)日：2023-03-16

申请号：US17988264

申请日：2022-11-16

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Ji LIU ,  Hong ZHANG ,  Juncheng JIA ,  Jiwen ZHOU ,  Shengbo PENG ,  Ruipu ZHOU ,  Dejing DOU

IPC: G06N20/00 ,  G06F9/54

Abstract: A federated learning method and system, an electronic device, and a storage medium, which relate to a field of artificial intelligence, in particular to fields of computer vision and deep learning technologies. The method includes: performing a plurality of rounds of training until a training end condition is met, to obtain a trained global model; and publishing the trained global model to a plurality of devices. Each of the plurality of rounds of training includes: transmitting a current global model to at least some devices in the plurality of devices; receiving trained parameters for the current global model from the at least some devices; performing an aggregation on the received parameters to obtain a current aggregation model; and adjusting the current aggregation model based on a globally shared dataset, and updating the adjusted aggregation model as a new current global model for a next round of training.

公开(公告)号：US20230206123A1

公开(公告)日：2023-06-29

申请号：US18080803

申请日：2022-12-14

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Ji LIU ,  Hong ZHANG ,  Juncheng JIA ,  Ruipu ZHOU ,  Dejing DOU

IPC: G06N20/00 ,  G06F9/48

CPC classification number: G06N20/00 ,  G06F9/4881

Abstract: A technical solution relates to distributed machine learning, and relates to the field of artificial intelligence technologies, such as machine learning technologies, or the like. An implementation includes: acquiring, based on delay information, an optimal scheduling queue of a plurality of edge devices participating in training; and scheduling each edge device of the plurality of edge devices to train a machine learning model based on the optimal scheduling queue of the plurality of edge devices.