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及其他用户的存在，以便为新设备做出频谱分配决策。简单的频谱占用检测方法往往可靠性不足，因此基于机器学习或人工智能的频谱占用检测算法被广泛且成功地应用。为保护用户数据隐私并减少控制数据量，联邦机器学习是一种值得关注的方法。本文比较了采用联邦机器学习的两种系统设计方法：含中心节点与不含中心节点。