Federated learning is a form of distributed model training where data remains on client devices. This means data is not passed directly to the coordinating server. By implication, models learn (ie train) using personal data sets without actually seeing the underlying data. Currently there are limited results from federated learning using models actually in production. However, in a world of GDPR and increased data protection legislation overall, this model training methodology is likely to receive much more attention in the world of AI and machine learning.
See an example of federated learning approach with R and Tensorflow at the link below.
