End-Edge Model Collaboration: Bandwidth Allocation for Data Upload and Model Transmission

Abstract

The widespread adoption of large artificial intelligence (AI) models has enabled numerous applications of the Internet of Things (IoT). However, large AI models require substantial computational and memory resources, which exceed the capabilities of resource-constrained IoT devices. End-edge collaboration paradigm is developed to address this issue, where a small model on the end device performs inference tasks, while a large model on the edge server assists with model updates. To improve the accuracy of the inference tasks, the data generated on the end devices will be periodically uploaded to edge server to update model, and a distilled model of the updated one will be transmitted back to the end device. Subjected to the limited bandwidth for the communication link between the end device and the edge server, it is important to investigate whether the system should allocate more bandwidth to data upload or to model transmission. In this paper, we characterize the impact of data upload and model transmission on inference accuracy. Subsequently, we formulate a bandwidth allocation problem. By solving this problem, we derive an efficient optimization framework for the end-edge collaboration system. The simulation results demonstrate our framework significantly enhances mean average precision (mAP) under various bandwidths and datasizes.