Distributed Learning Meets 6G: A Communication and Computing Perspective
It addresses the challenge of meeting stringent key performance indicators in 6G cellular networks for telecommunications and edge computing applications, but it is incremental as it builds on existing DL and FL frameworks.
This article explores how Distributed Learning (DL) and Federated Learning (FL) can help achieve 6G network performance goals by balancing communication and computing constraints, using Multi-Agent Reinforcement Learning (MARL) within FL for Dynamic Spectrum Access as a practical example with preliminary results.
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 towards 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 lays an outline of how DL in general and FL-based strategies specifically can contribute towards realizing a 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 (MARL) 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.