公开(公告)号：US20240296344A1

公开(公告)日：2024-09-05

申请号：US18647453

申请日：2024-04-26

Applicant: Huawei Technologies Co., Ltd.

Inventor： Shuo Wan ,  Jiaxun Lu ,  Chenghui Peng ,  Zhe Liu

IPC: G06N3/098

CPC classification number: G06N3/098

Abstract: A federated learning method includes a first network device that obtains local training data of at least two terminal devices; processes the local training data of the at least two terminal devices, to obtain training datasets; performs model training based on the training datasets, to obtain a model gradient; and sends the model gradient to a second network device. In this way, training data of the first network device is from the at least two terminal devices.

公开(公告)号：US20250156726A1

公开(公告)日：2025-05-15

申请号：US19022480

申请日：2025-01-15

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yunfeng Shao ,  Bingshuai Li ,  Jiaxun Lu ,  Zhenzhe Zheng ,  Fan Wu ,  Dahai Hu

IPC: G06N3/098

Abstract: A federated learning method includes a central node that separately sends a first model to at least one central edge device, receives at least one second model, and aggregates the at least one second model to obtain a fourth model. The at least one central edge device is in one-to-one correspondence with at least one edge device group. The second model is obtained by aggregating a third model respectively obtained by each edge device in at least one edge device group. The third model is obtained by one edge device in collaboration with at least one terminal device in a coverage area through learning the first model based on local data. The edge devices are grouped into edge device groups, and a central edge device in one edge device group sends the first model to each edge device in the edge device group.