  A vast amount of data is created every minute, both in the private sector and
industry. Whereas it is often easy to get hold of data in the private
entertainment sector, in the industrial production environment it is much more
difficult due to laws, preservation of intellectual property, and other
factors. However, most machine learning methods require a data source that is
sufficient in terms of quantity and quality. A suitable way to bring both
requirements together is federated learning where learning progress is
aggregated, but everyone remains the owner of their data. Federate learning was
first proposed by Google researchers in 2016 and is used for example in the
improvement of Google's keyboard Gboard. In contrast to billions of android
users, comparable machinery is only used by few companies. This paper examines
which other constraints prevail in production and which federated learning
approaches can be considered as a result.
