Federated Data Model
Xiao Chen Shunan Zhang Eric Z. Chen Yikang Liu Lin Zhao Terrence Chen Shanhui Sun
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.