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

• URL: http://arxiv.org/abs/2302.00623v1
• Date: Thu, 12 Jan 2023 10:30:43 GMT
• Title: Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks
• Authors: Fadhel Ayed, Antonio De Domenico, Adrian Garcia-Rodriguez, David Lopez-Perez
• Abstract summary: 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. We present a novel communication-aware ML framework, which enables an efficient AI/ML model transfer thanks to an overhauled model training and communication protocol.
• Score: 8.296411540693706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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