Federated Learning Methods, Applications and Beyond

Abstract

In recent years the applications of machine learning models have increased rapidly, due to the large amount of available data and technological progress. While some domains like web analysis can benefit from this with only minor restrictions, other fields like medicine with patient data are stronger regulated. In particular data privacy plays an important role as recently highlighted by the trustworthy AI initiative of the EU or general privacy regulations in legislation. Another major challenge is, that the required training data is often distributed in terms of features or samples and unavailable for classical batch learning approaches. In 2016 Google came up with a framework, called Federated Learning to solve both of these problems. We provide a brief overview on existing Methods and Applications in the field of vertical and horizontal Federated Learning, as well as Federated Transfer Learning.