Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
This addresses privacy and collaboration issues in health informatics for medical prediction tasks, though it appears incremental as it builds on existing distributed learning approaches.
The paper tackled the challenge of training deep learning models across siloed healthcare organizations while preserving patient privacy, by proposing a split learning framework that achieves performance similar to centralized and federated methods with improved computational efficiency and reduced privacy risks.
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks.