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：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.