Online Domain Adaptive Classification for Mobile-to-Edge Computing

A key challenge of today's systems is the mismatch between the high computational demands of modern neural network models for data analysis and the severely limited resources of mobile devices. Existing solutions focus on model simplification and task offloading to compute-capable edge servers. The former often leads to performance degradation, whereas the latter requires the transfer of information-rich signals and is subject to the impairments of wireless channels. To address these issues, a framework that establishes a novel form of collaboration between mobile devices and edge servers is proposed herein. The core idea is to deploy lightweight models on mobile devices that are intelligently updated to match the current, and local, distribution of the samples being observed. The framework develops the temporal patterns of the samples to determine the optimal model update policy, as well as channel resources allocated to the mobile users. The performance of the proposed framework is evaluated via extensive experiments with both synthetic and real-world datasets.