I. Introduction
Today, billions of mobile and Internet of Things (IoT) devices generate zillions bytes of data at the network edge, offering opportunities to deploy artificial intelligence (AI) locally on edge devices . Such on-device AI applications, e.g. deep neural networks (DNNs), have the advantage of avoiding transmitting raw data and hence preserving data privacy [63]. At the same time, the arising new challenge is that the environment continuously evolves, requiring the DNN models to retrain and adapt to those changes [6]. For example, Figure 1 illustrates the DNN model in client 1 needs to handle a sequence of tasks (e.g. image classification or object detection) over time. Typically, a task is composed of multiple classes/objects (e.g. different animals or vehicles) and different features for each class [41].