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.

翻译：动态频谱接入系统通常需要了解频谱占用情况，以确定新设备的频谱分配决策。 简单的频谱占用检测方法往往不可靠，因此通常使用受机器学习或人工智能支持的频谱占用检测算法。 为了保护用户数据的隐私并减少控制数据的量，可以使用联邦学习方法。 本文比较了两种使用联邦学习的系统设计方法：有中央节点和无中央节点。