Eric W. Tramel

Eric W. Tramel

Eric Tramel discusses the basic concepts underlying the federated Machine Learning approach, the advantages it brings, as well as the challenges associated with constructing federated solutions. Federated learning could be an interface between data science, machine learning, engineering, DevOps, software data, and security engineering, and bringing all this together in one spot.For machine learning test, the compute is happening where the data is, so now we have some other extra complications, we need to bring the compute to the data.If it's data that's generated in a hospital, never take it out of the hospital, leave it there.Eric is the head of Owkin’s Federated Learning Research group, where he heads a team of researchers and engineers to study and build applications of federated and privacy-aware machine learning techniques to medical data.

Federating learning: rewards and challenges of distributed private ml

Level: General