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 principles and, thereby, present communication-efficient and distributed learning frameworks with selected use cases.

中文翻译：

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无线网络上的高效通信和分布式学习：原理和应用

机器学习（ML）是第五代（5G）及以后的通信系统的有希望的推动力。通过将智能注入网络边缘，边缘节点可以主动执行决策，从而对本地环境变化和干扰做出反应，同时实现零通信延迟。为了实现这一目标，至关重要的是，通过不断地以分布式方式交换新鲜数据和ML模型更新，在时变的信道和网络动态条件下大规模地满足较高的ML推理精度。驯服这种新型的数据流量归结为通过优化通信有效负载类型，传输技术和调度以及ML体系结构，算法和数据处理方法来提高分布式学习的通信效率。为此，