公开(公告)号：US20230084055A1

公开(公告)日：2023-03-16

申请号：US17991958

申请日：2022-11-22

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

Inventor： Ji LIU ,  Sunjie YU ,  Dejing DOU ,  Jiwen ZHOU

IPC: G06N3/08 ,  G06F11/14

Abstract: A method for generating a federated learning model is provided. The method includes obtaining images; obtaining sorting results of the images; and generating a trained federated learning model by training a federated learning model to be trained according to the images and the sorting results. The federated learning model to be trained is obtained after pruning a federated learning model to be pruned, and a pruning rate of a convolution layer in the federated learning model to be pruned is automatically adjusted according to a model accuracy during the pruning.

公开(公告)号：US20230080230A1

公开(公告)日：2023-03-16

申请号：US17991977

申请日：2022-11-22

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

Inventor： Ji LIU ,  Sunjie YU ,  Dejing DOU ,  Jiwen ZHOU

IPC: G06N3/08 ,  G06F11/14

Abstract: A method for generating a federated learning model is provided. The method includes obtaining images; obtaining sorting results of the images; and generating a trained federated learning model by training a federated learning model to be trained according to the images and the sorting results. The federated learning model to be trained is obtained after pruning a federated learning model to be pruned, and a pruning rate of a convolution layer in the federated learning model to be pruned is automatically adjusted according to a model accuracy during the pruning.

公开(公告)号：US20230074417A1

公开(公告)日：2023-03-09

申请号：US18055149

申请日：2022-11-14

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

Inventor： Ji LIU ,  Sunjie YU ,  Jiwen ZHOU ,  Ruipu ZHOU ,  Dejing DOU

IPC: G06N3/08 ,  G06N3/04

Abstract: A method for training a longitudinal federated learning model is provided, and is applied to a first participant device. The first participant device includes label data. The longitudinal federated learning model includes a first bottom layer sub-model, an interaction layer sub-model, a top layer sub-model based on a Lipschitz neural network and a second bottom layer sub-model in a second participant device. First bottom layer output data of the first participant device and second bottom layer output data sent by the second participant device are obtained. The first bottom layer output data and the second bottom layer output data are input into an interaction layer sub-model to obtain interaction layer output data. Top layer output data is obtained based on the interaction layer output data and the top layer sub-model. The longitudinal federated learning model is trained according to the top layer output data and the label data.