- 专利标题: Training federated learning models
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申请号: US17402764申请日: 2021-08-16
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公开(公告)号: US12008075B2公开(公告)日: 2024-06-11
- 发明人: Shoichiro Watanabe , Kenichi Takasaki , Mari Abe Fukuda , Sanehiro Furuichi , Yasutaka Nishimura
- 申请人: International Business Machines Corporation
- 申请人地址: US NY Armonk
- 专利权人: International Business Machines Corporation
- 当前专利权人: International Business Machines Corporation
- 当前专利权人地址: US NY Armonk
- 代理机构: Edell, Shapiro & Finnan, LLC
- 主分类号: G06F18/214
- IPC分类号: G06F18/214 ; G06F17/18 ; G06F18/21 ; G06N3/08
摘要:
A computer system trains a federated learning model. A federated learning model is distributed to a plurality of computing nodes, each having a set of local training data comprising labeled data samples. Statistical data is received from each computing node that indicates the node's count of data samples for each label, and is analyzed to identify one or more computing nodes having local training data in which a label category is underrepresented beyond a threshold value with respect to data samples. Additional data samples labeled with the underrepresented labels are provided, and the computing nodes perform training. Results of training are received and are processed to generate a trained global model. Embodiments of the present invention further include a method and program product for training a federated learning model in substantially the same manner described above.
公开/授权文献
- US20230050708A1 TRAINING FEDERATED LEARNING MODELS 公开/授权日:2023-02-16
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