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

The First IEEE INFOCOM Workshop on Distributed Machine Learning and Fog Networks (FOGML 21)
Author: G. Drainakis (ICCS), K.V. Katsaros (ICCS), V. Sourlas (ICCS), A. Amditis (ICCS)
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.