The conventional deep learning paradigm often involves training a deep model
on a server and then deploying the model or its distilled ones to
resource-limited edge devices. Usually, the models shall remain fixed once
deployed (at least for some period) due to the potential high cost of model
adaptation for both the server and edge sides. However, in many real-world
scenarios, the test environments may change dynamically (known as distribution
shifts), which often results in degraded performance. Thus, one has to adapt
the edge models promptly to attain promising performance. Moreover, with the
increasing data collected at the edge, this paradigm also fails to further
adapt the cloud model for better performance. To address these, we encounter
two primary challenges: 1) the edge model has limited computation power and may
only support forward propagation; 2) the data transmission budget between cloud
and edge devices is limited in latency-sensitive scenarios. In this paper, we
establish a Cloud-Edge Elastic Model Adaptation (CEMA) paradigm in which the
edge models only need to perform forward propagation and the edge models can be
adapted online. In our CEMA, to reduce the communication burden, we devise two
criteria to exclude unnecessary samples from uploading to the cloud, i.e.,
dynamic unreliable and low-informative sample exclusion. Based on the uploaded
samples, we update and distribute the affine parameters of normalization layers
by distilling from the stronger foundation model to the edge model with a
sample replay strategy. Extensive experimental results on ImageNet-C and
ImageNet-R verify the effectiveness of our CEMA.

基于云边弹性模型适应（CEMA）范式，本文提出了一个在边缘设备上进行深度学习模型自适应的方法，通过前向传播和样本重放策略，更新和分发规范化层的仿射参数，以适应动态变化的环境，从而提高性能并减少通信负担。