Distributed Learning Meets 6G: A Communication and Computing Perspective
Shashank Jere, Yifei Song, Yang Yi, Lingjia Liu
Abstract
With the ever improving computing capabilities and storage capacities of mobile devices in line with evolving telecommunication network paradigms, there has been an explosion of research interest toward exploring distributed learning (DL) frameworks to realize stringent key performance indicators (KPIs) that are expected in next-generation/6G cellular networks. In conjunction with edge computing, federated learning (FL) has emerged as the DL architecture of choice in prominent wireless applications. This article provides an outline of how DL in general and FL-based strategies specifically can contribute toward realizing part of the 6G vision and strike a balance between communication and computing constraints. As a practical use case, we apply multi-agent reinforcement learning within the FL framework to the dynamic spectrum access (DSA) problem and present preliminary evaluation results. Top contemporary challenges in applying DL approaches to 6G networks are also highlighted.