Federated Learning -- Methods, Applications and beyond

TL;DR

Federated Learning enables collaborative machine learning across distributed data sources while preserving privacy, with diverse methods and applications in various domains, including transfer learning.

Contribution

This paper provides an overview of federated learning methods, applications, and extensions like transfer learning, highlighting recent developments and challenges.

Findings

Federated Learning addresses privacy concerns in distributed data environments.

It has diverse applications in healthcare, finance, and web analysis.

Various methods and transfer learning extensions are actively researched.

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 in medicine with patient data are strongerregulated. In particular \emph{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 \emph{data is} often \emph{distributed} in terms of features or samples and unavailable for classicalbatch learning approaches. In 2016 Google came up with a framework, called \emph{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 \emph{Federated Learning}, as well as \emph{Fderated Transfer Learning}.