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.

利用 Federated Data Model (FDM) 方法开发了一种能够在不同地点之间训练准确且注重隐私的人工智能模型的方法，通过扩散模型学习一处地点的数据特征，并生成可用于其他地点的合成数据，成功地应用于医学图像分割任务。