公开(公告)号：US20230162087A1

公开(公告)日：2023-05-25

申请号：US17989243

申请日：2022-11-17

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

Inventor： Ji LIU ,  Chendi ZHOU ,  Beichen MA ,  Jiwen ZHOU ,  Dejing DOU

IPC: G06N20/00 ,  G06F9/48

CPC classification number: G06N20/00 ,  G06F9/4881

Abstract: A federated learning method, an electronic device, and a storage medium, which relate to a field of artificial intelligence, in particular to fields of distributed data processing and deep learning. The method includes: determining, for each task in a current learning period, a set of target devices corresponding to the task according to respective scheduling information of a plurality of candidate devices corresponding to the task based on a scheduling policy, the scheduling policy enables a time cost information and a device fairness evaluation information of completing the task in the current learning period to meet a predetermined condition; transmitting a global model corresponding to each task to a set of target devices corresponding to the task; and updating the corresponding global model based on trained models in response to receiving the trained models from the corresponding set of target devices.

公开(公告)号：US20220383198A1

公开(公告)日：2022-12-01

申请号：US17879888

申请日：2022-08-03

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

Inventor： Ji LIU ,  Chendi ZHOU ,  Shilei JI ,  Dejing DOU

IPC: G06N20/00 ,  G06Q10/04 ,  G06Q10/06

Abstract: The present disclosure provides a method for asynchronous federated learning, including: in response to a request for participating in asynchronous federated learning sent by a target electronic device, determining, according to performance information of a server, a first number of electronic devices that the server supports to participate in the asynchronous federated learning, and acquiring a second number of other electronic devices that have participated in the asynchronous federated learning; if the first number is greater than the second number, sending a global model to be optimized to the target electronic device, and receiving target feedback information which is obtained by the target electronic device from training on the global model to be optimized; and optimizing, according to the target feedback information, the global model to be optimized to obtain an optimized global model.

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：US20220374776A1

公开(公告)日：2022-11-24

申请号：US17868113

申请日：2022-07-19

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

Inventor： Ji LIU ,  Beichen MA ,  Chendi ZHOU ,  Jingbo ZHOU ,  Ruipu ZHOU ,  Dejing DOU

IPC: G06N20/00

Abstract: The present disclosure provides a method and apparatus for federated learning, which relate to the technical fields such as big data and deep learning. A specific implementation is: generating, for each task in a plurality of different tasks trained simultaneously, a global model for each task; receiving resource information of each available terminal in a current available terminal set; selecting a target terminal corresponding to each task from the current available terminal set, based on the resource information and the global model; and training the global model using the target terminal until a trained global model for each task meets a preset condition.

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.