Federated Data Model

arXiv:2403.08887v1 Announce Type: new
Abstract: 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 …

abstract applications artificial artificial intelligence arxiv challenges cs.cv data data diversity data model data privacy deep learning development diversity however intelligence locations medical model development pivotal privacy regulations robust role sharing data training type