Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks

TL;DR

This paper introduces Accordion, a communication-aware machine learning framework designed for efficient AI/ML model transfer in future 5G-based networks, optimizing communication protocols and reducing overheads.

Contribution

The paper proposes a novel communication-aware ML framework, Accordion, with an improved training and communication protocol tailored for next-generation network infrastructures.

Findings

Accordion reduces communication overheads in AI/ML model transfer.

The framework demonstrates improved efficiency in 5G network scenarios.

Trade-offs between communication cost and model performance are analyzed.

Abstract

In this article, we advocate for the design of ad hoc artificial intelligence (AI)/machine learning (ML) models to facilitate their usage in future smart infrastructures based on communication networks. To motivate this, we first review key operations identified by the 3GPP for transferring AI/ML models through 5G networks and the main existing techniques to reduce their communication overheads. We also present a novel communication-aware ML framework, which we refer to as Accordion, that enables an efficient AI/ML model transfer thanks to an overhauled model training and communication protocol. We demonstrate the communication-related benefits of Accordion, analyse key performance trade-offs, and discuss potential research directions within this realm.