Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks
This addresses communication bottlenecks for future smart infrastructures using 5G networks, but it appears incremental as it builds on existing techniques.
The paper tackles the problem of inefficient AI/ML model transfer in 5G networks by proposing Accordion, a communication-aware framework that reduces communication overhead through an overhauled training and protocol, though no concrete performance numbers are provided.
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.