Federated Data Model
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911797549727744 |
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| author | Chen, Xiao Zhang, Shunan Chen, Eric Z. Liu, Yikang Zhao, Lin Chen, Terrence Sun, Shanhui |
| author_facet | Chen, Xiao Zhang, Shunan Chen, Eric Z. Liu, Yikang Zhao, Lin Chen, Terrence Sun, Shanhui |
| contents | In artificial intelligence (AI), especially deep learning, data diversity and volume play a pivotal role in model development. However, training a robust deep learning model often faces challenges due to data privacy, regulations, and the difficulty of sharing data between different locations, especially for medical applications. To address this, we developed a method called the Federated Data Model (FDM). This method uses diffusion models to learn the characteristics of data at one site and then creates synthetic data that can be used at another site without sharing the actual data. We tested this approach with a medical image segmentation task, focusing on cardiac magnetic resonance images from different hospitals. Our results show that models trained with this method perform well both on the data they were originally trained on and on data from other sites. This approach offers a promising way to train accurate and privacy-respecting AI models across different locations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08887 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Data Model Chen, Xiao Zhang, Shunan Chen, Eric Z. Liu, Yikang Zhao, Lin Chen, Terrence Sun, Shanhui Computer Vision and Pattern Recognition In artificial intelligence (AI), especially deep learning, data diversity and volume play a pivotal role in model development. However, training a robust deep learning model often faces challenges due to data privacy, regulations, and the difficulty of sharing data between different locations, especially for medical applications. To address this, we developed a method called the Federated Data Model (FDM). This method uses diffusion models to learn the characteristics of data at one site and then creates synthetic data that can be used at another site without sharing the actual data. We tested this approach with a medical image segmentation task, focusing on cardiac magnetic resonance images from different hospitals. Our results show that models trained with this method perform well both on the data they were originally trained on and on data from other sites. This approach offers a promising way to train accurate and privacy-respecting AI models across different locations. |
| title | Federated Data Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.08887 |