公开(公告)号：US20230297888A1

公开(公告)日：2023-09-21

申请号：US18184011

申请日：2023-03-15

申请人： CAE Inc.

发明人： Jean-François Delisle ,  Ben Winokur ,  Navpreet Singh

IPC分类号： G06N20/00

CPC分类号： G06N20/00

摘要： A federated machine learning system for training students comprises a first adaptive training system having a first artificial intelligence module for adapting individualized training to a first group of students and for developing a first learning model based on a first set of learning performance metrics. A second adaptive training system provides individualized training to a second group of students and has a data property extraction module for extracting statistical properties from a second set of learning performance metrics for the second group of students. A data simulator module generates simulated performance metrics using extracted statistical properties from the second set of learning performance metrics to thereby generate a second learning model. A federation computing device receives first and second model weights for the first and second learning models and generates or refines a federated model based on the first and second model weights.

公开(公告)号：US11915111B2

公开(公告)日：2024-02-27

申请号：US18184011

申请日：2023-03-15

申请人： CAE Inc.

发明人： Jean-François Delisle ,  Ben Winokur ,  Navpreet Singh

IPC分类号： G06N20/00

CPC分类号： G06N20/00

摘要： A federated machine learning system for training students comprises a first adaptive training system having a first artificial intelligence module for adapting individualized training to a first group of students and for developing a first learning model based on a first set of learning performance metrics. A second adaptive training system provides individualized training to a second group of students and has a data property extraction module for extracting statistical properties from a second set of learning performance metrics for the second group of students. A data simulator module generates simulated performance metrics using extracted statistical properties from the second set of learning performance metrics to thereby generate a second learning model. A federation computing device receives first and second model weights for the first and second learning models and generates or refines a federated model based on the first and second model weights.