  When dealing with deep neural network (DNN) applications on edge devices,
continuously updating the model is important. Although updating a model with
real incoming data is ideal, using all of them is not always feasible due to
limits, such as labeling and communication costs. Thus, it is necessary to
filter and select the data to use for training (i.e., active learning) on the
device. In this paper, we formalize a practical active learning problem for
DNNs on edge devices and propose a general task-agnostic framework to tackle
this problem, which reduces it to a stream submodular maximization. This
framework is light enough to be run with low computational resources, yet
provides solutions whose quality is theoretically guaranteed thanks to the
submodular property. Through this framework, we can configure data selection
criteria flexibly, including using methods proposed in previous active learning
studies. We evaluate our approach on both classification and object detection
tasks in a practical setting to simulate a real-life scenario. The results of
our study show that the proposed framework outperforms all other methods in
both tasks, while running at a practical speed on real devices.
