- 专利标题: Neural network trained using federated learning with local training data preserved at local edge circuits
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申请号: US16676314申请日: 2019-11-06
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公开(公告)号: US12072954B1公开(公告)日: 2024-08-27
- 发明人: Wenqi Li , Fausto Milletari , Daguang Xu , Yan Cheng , Nicola Christin Rieke , Charles Jonathan Hancox , Wentao Zhu , Rong Ou , Andrew Feng
- 申请人: NVIDIA Corporation
- 申请人地址: US CA Santa Clara
- 专利权人: NVIDIA Corporation
- 当前专利权人: NVIDIA Corporation
- 当前专利权人地址: US CA Santa Clara
- 代理机构: Davis Wright Tremaine LLP
- 主分类号: G06T7/00
- IPC分类号: G06T7/00 ; G06F7/57 ; G06F18/214 ; G06N3/045 ; G06N3/063 ; G06N3/08 ; G06V10/94 ; G16H30/20
摘要:
Apparatuses, systems, and techniques to perform federated training of neural networks while maintaining control over dissemination of local models of neural networks from which aspects of local training data might be extracted. In at least one embodiment, a neural network is trained on local training data and a local model is provided to be aggregated with other local models into a global model that is in turn used for further local model training, wherein a provided local model or training is adjusted to reduce an ability to extract aspects of local training data therefrom.
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