Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into network edge, edge nodes can proactively carry out decision-making and, thereby, react to local environmental changes disturbances while experiencing zero latency. To achieve this goal, it essential cater high ML inference accuracy at scale under time-varying channel dynamics, by continuously exchanging fresh data model updates in distributed way. Taming new kind of traffic boils down improving efficiency optimizing payload types, transmission techniques, scheduling, as well architectures, algorithms, processing methods. end, article aims provide holistic overview relevant principles present communication-efficient frameworks with selected use cases.