作者
Jihong Park, Sumudu Samarakoon, Anis Elgabli, Joongheon Kim, Mehdi Bennis, Seong-Lyun Kim, Mérouane Debbah
发表日期
2021/2/18
期刊
Proceedings of the IEEE
卷号
109
期号
5
页码范围
796-819
出版商
IEEE
简介
Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making and, thereby, react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under the time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML …
学术搜索中的文章
Communication-efficient and distributed learning over wireless networks: Principles and applications
J Park, S Samarakoon, A Elgabli, J Kim, M Bennis… - Proceedings of the IEEE, 2021
Communication-efficient and distributed learning over wireless networks: Principles and applications
J Park, S Samarakoon, A Elgabli, J Kim, M Bennis… - arXiv preprint arXiv:2008.02608, 2020