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.

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：US20220391780A1

公开(公告)日：2022-12-08

申请号：US17820758

申请日：2022-08-18

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Ji LIU ,  Beichen MA ,  Chendi ZHOU ,  Juncheng JIA ,  Dejing DOU ,  Shilei JI ,  Yuan LIAO

IPC: G06N20/20 ,  G06F9/48

Abstract: The present disclosure provides a method of federated learning. A specific implementation solution includes: determining, for a current learning period, a target device for each task of at least one learning task to be performed, from a plurality of candidate devices according to a plurality of resource information of the plurality of candidate devices; transmitting a global model for the each task to the target device for the each task, so that the target device for the each task trains the global model for the each task; and updating, in response to receiving trained models from all target devices for the each task, the global model for the each task according to the trained models, so as to complete the current learning period. The present disclosure further provides an electronic device, and a storage medium.

公开(公告)号：US20220366320A1

公开(公告)日：2022-11-17

申请号：US17864098

申请日：2022-07-13

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

Inventor： Ji LIU ,  Chendi ZHOU ,  Juncheng JIA ,  Dejing DOU

IPC: G06N20/20

Abstract: A computer-implemented method is provided. The method includes: executing, for each task in a federated learning system, a first training process comprising: obtaining resource information of a plurality of terminal devices of the federated learning system; determining one or more target terminal devices corresponding to the task based on the resource information; and training a global model corresponding to the task by the target terminal devices until the global model meets a preset condition.