  Dynamic spectrum access systems typically require information about the
spectrum occupancy and thus the presence of other users in order to make a
spectrum al-location decision for a new device. Simple methods of spectrum
occupancy detection are often far from reliable, hence spectrum occupancy
detection algorithms supported by machine learning or artificial intelligence
are often and successfully used. To protect the privacy of user data and to
reduce the amount of control data, an interesting approach is to use federated
machine learning. This paper compares two approaches to system design using
federated machine learning: with and without a central node.
