Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks
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Abstract (arXiv)
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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References · 8
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
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- 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
- 2017Distributed Deep Neural Networks over the Cloud, the Edge and End Devicescited by 119
- 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
- 2020Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscited by 62
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