Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
This work addresses the problem of enabling intelligent edge computing in 5G and beyond networks for applications requiring low latency and adaptability, but it is incremental as it synthesizes existing principles into frameworks.
The paper tackles the challenge of achieving high machine learning inference accuracy at scale in wireless networks under dynamic conditions by optimizing communication efficiency in distributed learning. It provides a holistic overview of principles and frameworks for communication-efficient distributed learning, with selected use cases.
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 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.