公开(公告)号：US20240086720A1

公开(公告)日：2024-03-14

申请号：US18518753

申请日：2023-11-24

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yinchuan LI ,  Yunfeng SHAO ,  Li QIAN

IPC: G06N3/098

CPC classification number: G06N3/098

Abstract: This application provides a federated learning method, apparatus, and system, so that a server retrains a received model in a federated learning process to implement depersonalization processing to some extent, to obtain a model with higher output precision. The method includes: First, a first server receives information about at least one first model sent by at least one downstream device, where the at least one downstream device may include another server or a client connected to the first server; the first server trains the at least one first model to obtain at least one trained first model; and then the first server aggregates the at least one trained first model, and updates a locally stored second model by using an aggregation result, to obtain an updated second model.

公开(公告)号：US20250068921A1

公开(公告)日：2025-02-27

申请号：US18944331

申请日：2024-11-12

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yinchuan LI ,  Yunfeng SHAO ,  Wenqian LI

IPC: G06N3/092 ,  G06N3/045

Abstract: A causality determining method relates to the field of artificial intelligence. The method includes: obtaining first information that is obtained by predicting a plurality of variables by a generative flow model and that indicates causality between the plurality of variables; and predicting second information of the plurality of variables based on the first information and by using the generative flow model, where the second information indicates that first causality exists between a first variable and a second variable in the plurality of variables, and the first information indicates that the first causality does not exist between the first variable and the second variable. This reduces computing capability overheads and improves a convergence speed of the model.