Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

TL;DR

This paper reviews principles and frameworks for communication-efficient distributed machine learning over wireless networks, focusing on optimizing data exchange, transmission, and ML architectures to support 5G and beyond.

Contribution

It provides a comprehensive overview of communication and ML principles, proposing frameworks and use cases for efficient distributed learning in wireless environments.

Findings

Enhanced communication payload optimization techniques

Effective scheduling and transmission strategies for distributed ML

Frameworks demonstrating improved learning efficiency in wireless networks

Abstract

Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making, and thereby react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases.