ESANN 2021 - European Symposium on Artificial Neural Networks,
Computational Intelligence and Machine Learning, Oct 2021, Online event
(Bruges), Belgium. pp.1-10
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}.
Moritz Heusinger, Christoph Raab, Fabrice Rossi, Frank-Michael Schleif
2022-12-22
