公开(公告)号：US20250061376A1

公开(公告)日：2025-02-20

申请号：US18489087

申请日：2023-10-18

Applicant: Korea Advanced Institute of Science and Technology

Inventor： Seyoung YUN ,  Seongyoon KIM ,  Minchan JEONG ,  Jaehoon OH ,  Gihun LEE

IPC: G06N20/00

Abstract: There is provided a federated learning system. The federated learning system comprises: a central server including a central learning model; and a plurality of client devices, each including a local learning model trained by performing federated learning with the central learning model, wherein the central server is configured to transmit status information of the central learning model to each client device, receive status information of the trained local learning model from each client device, and update the central learning model based on the status information of the trained local learning model, wherein each client device is configured to update the status information of the central learning model to the local learning model, train the local learning model by using individual training data, determine the status information of the trained local learning model, and transmit status information of the trained local learning model to the central server.

公开(公告)号：US20240112040A1

公开(公告)日：2024-04-04

申请号：US18472393

申请日：2023-09-22

Applicant: Korea Advanced Institute of Science and Technology

Inventor： Seyoung YUN ,  Seongyoon KIM ,  Woojin CHUNG ,  Sangmin BAE

IPC: G06N3/098 ,  G06N3/09

CPC classification number: G06N3/098 ,  G06N3/09

Abstract: A federated learning system includes: a central server including a central learning model containing an extractor; and client devices each including a local learning model performing federated learning with the central learning model. The local learning model includes an extractor and a classifier, and the central server transmits state information of the extractor in the central learning model to a client device among the client devices, receives state information of the extractor in the local learning model from the client device, and updates the extractor in the central learning model using the received state information, and the client device uploads the state information to the extractor in the local learning model, trains the extractor and the classifier in the local learning model using individual training data, and transmits state information of the extractor trained in the local learning model to the central server.