Porównanie metod detekcji zajętości widma radiowego z wykorzystaniem uczenia federacyjnego z oraz bez węzła centralnego
This work addresses privacy and data efficiency in spectrum management for wireless communication systems, but it appears incremental as it compares existing federated learning variants without introducing a new method.
The paper tackled the problem of improving spectrum occupancy detection for dynamic spectrum access systems by comparing two federated learning approaches—with and without a central node—to enhance reliability and privacy, but no concrete results or numbers are provided.
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.