DCP: a TCP-Inspired Method for Online Domain Adaptation under Dynamic Data Drift

Mobile computing faces challenges due to the resource constraints of mobile devices, such as limited computing power, energy, and connectivity. These limitations hinder the use of high-complexity classifiers and wireless transmissions. To address this issue, we propose a novel collaboration paradigm between mobile devices and edge servers, where the edge server assists the mobile devices by dynamically retraining a low-complexity classifier to adapt to temporal changes in data distribution. We propose a novel approach called drift control protocol (DCP) which is inspired by TCP congestion control mechanism. DCP aims to strike a balance between low-complexity classifier retraining frequency and communication costs with the edge server. It adjusts the update rate of the classifier on the mobile device based on distribution drift characteristics and controls the number of input samples sent to the edge server to improve accuracy. We evaluate and study different versions of DCP using synthetic and real datasets We demonstrate that DCP keeps the error bound, while reducing the burden of the communication cost by 90% for the mobile nodes, which makes our proposal suitable for online domain adaptation.