Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation

📄 arXiv: 2402.17316v3 📥 PDF

作者: Yaofo Chen, Shuaicheng Niu, Yaowei Wang, Shoukai Xu, Hengjie Song, Mingkui Tan

分类: cs.CV

发布日期: 2024-02-27 (更新: 2024-06-06)

备注: Published in ICLR 2024

💡 一句话要点

提出云边弹性模型适应方法以应对动态环境挑战

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 云边计算 模型适应 深度学习 样本选择 蒸馏训练 动态环境 边缘计算

📋 核心要点

  1. 现有方法在动态环境中难以适应，导致边缘模型性能下降，且云模型无法有效利用边缘收集的数据。
  2. 提出云边弹性模型适应（CEMA）范式，使边缘模型仅需进行前向传播并能在线适应，同时减少通信负担。
  3. 在ImageNet-C和ImageNet-R数据集上进行的实验验证了CEMA的有效性，显著提升了模型性能。

📝 摘要（中文）

传统深度学习方法通常在服务器上训练模型，然后将其或其蒸馏版本部署到资源有限的边缘设备上。然而，部署后的模型通常保持固定，难以适应动态变化的测试环境，导致性能下降。为了解决这一问题，本文提出了一种云边弹性模型适应（CEMA）范式，使边缘模型能够在线适应，同时减少云与边缘设备之间的通信负担。通过动态不可靠样本和低信息样本的排除策略，结合样本重放策略，更新和分发归一化层的仿射参数。实验结果表明，CEMA在ImageNet-C和ImageNet-R数据集上表现出色。

🔬 方法详解

问题定义：本文旨在解决边缘设备在动态环境中模型适应能力不足的问题。现有方法在模型部署后难以进行有效的适应，导致性能下降，且云与边缘之间的通信成本高。

核心思路：提出云边弹性模型适应（CEMA）范式，允许边缘模型在线进行适应，且仅需进行前向传播。通过排除不必要的样本来减少通信负担，从而提高适应效率。

技术框架：CEMA框架包括两个主要模块：样本选择模块和模型更新模块。样本选择模块负责动态排除不可靠和低信息样本，模型更新模块则基于上传的样本更新归一化层的仿射参数。

关键创新：本文的主要创新在于提出了动态不可靠样本和低信息样本的排除策略，显著降低了通信成本，同时实现了边缘模型的在线适应。这与传统方法的固定模型适应形成了鲜明对比。

关键设计：在模型更新过程中，采用样本重放策略来更新归一化层的仿射参数，确保边缘模型能够有效利用上传的样本信息。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，CEMA在ImageNet-C和ImageNet-R数据集上显著提升了模型性能，相较于基线方法，性能提升幅度达到XX%（具体数据待补充），验证了其有效性和优越性。

🎯 应用场景

该研究具有广泛的应用潜力，尤其在物联网、智能监控和自动驾驶等领域。通过提高边缘设备的模型适应能力，可以更好地应对动态环境变化，提升系统的智能化水平和响应速度，具有重要的实际价值和未来影响。

📄 摘要（原文）

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.