  This paper proposes Shoggoth, an efficient edge-cloud collaborative
architecture, for boosting inference performance on real-time video of changing
scenes. Shoggoth uses online knowledge distillation to improve the accuracy of
models suffering from data drift and offloads the labeling process to the
cloud, alleviating constrained resources of edge devices. At the edge, we
design adaptive training using small batches to adapt models under limited
computing power, and adaptive sampling of training frames for robustness and
reducing bandwidth. The evaluations on the realistic dataset show 15%-20% model
accuracy improvement compared to the edge-only strategy and fewer network costs
than the cloud-only strategy.
