Latest Trends in Wireless Network Optimization Using Distributed Learning

The demand for machine learning (ML) algorithms in wireless communication have increased over the last decade. Anyhow to enhance the prediction quality in complex problems, a significant amount of training is needed. ML requires centralized training which consumes more processing power. İn the case of massive networks, consequently, it is necessary to train and test the algorithms in a distributed manner, which results in distributed learning. Recent development and methodologies used for distributed learning in network optimization of wireless networks are presented in a comprehensive manner in this paper.