Selective Data Offloading in Edge Computing for Two-Tier Classification With Local Domain Partitions

We consider a two-tier approach for the classification of user-generated data, where low-complexity decision algorithms are available on mobile devices, and a better assessment can be performed on a shared edge server to which the samples can be offloaded. While an overall accurate classification can be achieved by either massive offloading to the edge server alone or performing a computationally intense domain partitioning for local evaluation, both these solutions taken individually are excessively demanding. Importantly, the former strategy achieves higher accuracy, yet is very bandwidth-consuming, while the latter results in lower accuracy while reducing bandwidth usage. To cope with these challenges, we take a quantitative stance to investigate the benefit of combining these two strategies, i.e., performing most of the evaluations with a local decision over constrained domains, while at the same time offloading to the edge server a small fraction of the samples for which the classification is expected to be less accurate. If properly harmonized, such an approach is shown to lead to a sharp increase in classification accuracy, with overall limited resource usage, which makes it suitable for practical implementations.