Federated Learning

Federated Learning is an emerging method in deep learning where multiple collaborators train a machine learning model in parallel, without trespassing institutional firewalls. This "decentralized" learning approach allows data to be kept within each collaborative institution protected servers, while their deep learning model updates are transferred to a central server to be aggregated in a consensus model. In contrast, the conventional "centralized" deep learning approach requires data to be uploaded to central servers, which becomes problematic when dealing with health data and patient-identifiers.

Outside of the medical world, federated learning is actively used by technologists to augment datasets feeding AI systems. It is currently used by Google to "build better AI products with on-device data and privacy by default". This novel approach has the potential to revolutionize health informatics and applications of artificial intelligence in medicine.