公开(公告)号：US20230116117A1

公开(公告)日：2023-04-13

申请号：US18080523

申请日：2022-12-13

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yunfeng Shao ,  Kaiyang Guo ,  Vincent Moens ,  Jun Wang ,  Chunchun Yang

IPC: G06N3/098

Abstract: A method includes: A second node sends a prior distribution of a parameter in a federated model to at least one first node. After receiving the prior distribution of the parameter in the federated model, the at least one first node performs training based on the prior distribution of the parameter in the federated model and local training data of the first node, to obtain a posterior distribution of a parameter in a local model of the first node. After the local training ends, the at least one first node feeds back the posterior distribution of the parameter in the local model to the second node, so that the second node updates the prior distribution of the parameter in the federated model based on the posterior distribution of the parameter in the local model of the at least one first node.

公开(公告)号：US20240394556A1

公开(公告)日：2024-11-28

申请号：US18795145

申请日：2024-08-05

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yunfeng Shao ,  Kaiyang Guo ,  Jun Wu

IPC: G06N3/098

Abstract: A machine learning model training method, a service data processing method, and an apparatus are provided, which are applied to the artificial intelligence field. In a training phase, a cloud server sends a machine learning submodel to an edge server. The edge server performs federated learning with client devices in a management domain of the edge server based on the obtained machine learning submodel, to obtain a trained machine learning submodel, and sends the trained machine learning submodel to the cloud server. The cloud server fuses obtained different trained machine learning submodels, to obtain a machine learning model. According to this application, training efficiency of the machine learning model can be improved. In an inference phase, the client device processes service data by using the trained machine learning submodel. According to this application, prediction efficiency of the machine learning model can be improved.