On the Distribution of ML Workloads to the Network Edge and Beyond

The emerging paradigm of edge computing has revolutionized network applications, delivering computational power closer to the end-user. Consequently, Machine Learning (ML) tasks, typically performed in a data centre (Centralized Learning - CL), can now be offloaded to the edge (Edge Learning - EL) or mobile devices (Federated Learning - FL). While the inherent flexibility of such distributed schemes has drawn considerable attention, a thorough investigation on their resource consumption footprint is still missing. In our work, we consider a FL scheme and two EL variants, representing varying proximity to the end users (data sources) and corresponding levels of workload distribution across the network; namely Access Edge Learning (AEL), where edge nodes are essentially co-located with the base stations and Regional Edge Learning (REL), where they lie towards the network core. Based on real systems' measurements and user mobility traces, we devise a realistic simulation model to evaluate and compare the performance of the considered ML schemes under an image classification task. Our results indicate that FL and EL can act as viable alternatives to CL. Edge learning effectiveness is shaped by the configuration of edge nodes in the network with REL achieving the prominent combination of accuracy and bandwidth needs. Energy-wise, edge learning is shown to offer an attractive choice (for involved stakeholders) to offload centralised ML tasks.