公开(公告)号：US12236370B2

公开(公告)日：2025-02-25

申请号：US17179964

申请日：2021-02-19

Applicant: Samsung Electronics Co., Ltd.

Inventor： Mostafa El-Khamy ,  Weituo Hao ,  Jungwon Lee

IPC: G06N7/00 ,  G06N7/01 ,  G06N20/00 ,  H04L67/10

Abstract: Methods and devices are provided for performing federated learning. A global model is distributed from a server to a plurality of client devices. At each of the plurality of client devices: model inversion is performed on the global model to generate synthetic data; the global model is on an augmented dataset of collected data and the synthetic data to generate a respective client model; and the respective client model is transmitted to the server. At the server: client models are received from the plurality of client devices, where each client model is received from a respective client device of the plurality of client devices; model inversion is performed on each client model to generate a synthetic dataset; the client models are averaged to generate an averaged model; and the averaged model is trained using the synthetic dataset to generate an updated model.

公开(公告)号：US20220058507A1

公开(公告)日：2022-02-24

申请号：US17179964

申请日：2021-02-19

Applicant: Samsung Electronics Co., Ltd.

Inventor： Mostafa El-Khamy ,  Weituo Hao ,  Jungwon Lee

IPC: G06N7/00 ,  G06N20/00 ,  H04L29/08

Abstract: Methods and devices are provided for performing federated learning. A global model is distributed from a server to a plurality of client devices. At each of the plurality of client devices: model inversion is performed on the global model to generate synthetic data; the global model is on an augmented dataset of collected data and the synthetic data to generate a respective client model; and the respective client model is transmitted to the server. At the server: client models are received from the plurality of client devices, where each client model is received from a respective client device of the plurality of client devices: model inversion is performed on each client model to generate a synthetic dataset; the client models are averaged to generate an averaged model; and the averaged model is trained using the synthetic dataset to generate an updated model.