Distributed Learning Meets 6G: A Communication and Computing Perspective

03/02/2023

∙

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.

READ FULL TEXT

Distributed Learning Meets 6G: A Communication and Computing Perspective

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices

TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments

Federated Dynamic Spectrum Access

A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning

Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System

SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks

Distributed Learning Meets 6G: A Communication and Computing Perspective

Related Research

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices

TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments

Federated Dynamic Spectrum Access

A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning

Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System

SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks