Conventional knowledge for machine learning and deep learning in medical data says that larger and more diverse data is needed to better train artificial intelligence (AI) models. However, one particularity in the medical data setting is that data sharing across different institutions is often complicated by strict privacy regulations and data-ownership concerns, making the collection of large-scale, diverse centralized datasets practically impossible.

Therefore, methods for training on large distributed datasets without sharing data and breaching restrictions on privacy and property, like Federated Learning (FL), are needed. Different institutions can build more robust models using FL by performing collaborative training without sharing raw training data. Though challenging, federated learning has gained increasing popularity since its inception and has mostly focused on the studies in general deep learning contexts.

This Special Issue aims to gather recent advances and novel contributions from academic researchers and industry practitioners on the vibrant topic of federated learning to achieve better development of deep learning methods in the field of smart medicine and human health. In addition, this Special Issue welcomes relevant researchers to discuss the latest developments in the feasibility of new applications of deep learning methods in healthcare management systems or software. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• Federated, distributed learning, and other forms of collaborative learning that are applicable to medical data
• Medical image analysis and distributed computing
• Medical image segmentation and federated learning
• Medical image annotation for federated learning
• Privacy-preserving and security techniques for FL for medical data
• Feature learning of medical images with high-dimensional isolated samples
• Disease screening in federated learning
• Clinical decision and deep learning
• Personal health data analysis
• Intelligent health management based on data processing and chip technology
• Dealing with heterogeneous and unbalanced (non-IID) data in FL for medical data