Distributed Learning for Wi-Fi AP Load Prediction

arXiv:2405.05140v1 Announce Type: cross
Abstract: The increasing cloudification and softwarization of networks foster the interplay among multiple independently managed deployments. An appealing reason for such an interplay lies in distributed Machine Learning (ML), which allows the creation of robust ML models by leveraging collective intelligence and computational power. In this paper, we study the application of the two cornerstones of distributed learning, namely Federated Learning (FL) and Knowledge Distillation (KD), on the Wi-Fi Access Point (AP) load prediction use case. …

abstract arxiv collective computational cs.ai cs.lg cs.ni deployments distributed distributed learning intelligence lies machine machine learning managed ml models multiple networks paper power prediction reason robust study type wi-fi