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

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. Artificial intelligence (AI) and machine learning (ML) are becoming ubiquitous in our lives. At a consumer level, most used applications in our smartphones rely on AI/ML algorithms for image recognition and video editing. Due to their benefits, vendors and operators are also currently investing on AI/ML models to plan, design, and operate their fifth generation (5G) networks [1]. From an operational perspective, the integration of neural processing units and the execution of AI/ML models in smartphones are becoming commonplace to satisfy the latency constraints of complex applications and/or safeguard their data privacy.