Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.

Federated learning (FL) is a privacy preserving approach to learning that overcome issues related to data access, privacy, and security, which represent key challenges in the healthcare sector. FL enables hospitals to collaboratively learn a shared prediction model without moving the data outside their secure infrastructure. To do so, after having sent model updates to a central server, an update aggregation is performed, and the model is sent back to the sites for further training.

Privacy Enhancing and Scalable Federated Learning to Accelerate AI Implementation in Cross-Silo and IoMT Environments.

Federated Learning (FL) is a machine learning technique that enables to collaboratively learn valuable information across devices or sites without moving the data. In FL, the model is trained and shared across decentralized locations where data are privately owned. After local training, model updates are sent back to a central server, thus enabling access to distributed data on a large scale while maintaining privacy, security, and data access rights.