Device Model Generalization (DMG) is a practical yet under-investigated research topic for on-device machine learning applications. It aims to improve the generalization ability of pre-trained models when deployed on resource-constrained devices, such as improving the performance of pre-trained cloud models on smart mobiles. While quite a lot of works have investigated the data distribution shift across clouds and devices, most of them focus on model fine-tuning on personalized data for individual devices to facilitate DMG. Despite their promising, these approaches require on-device re-training, which is practically infeasible due to the overfitting problem and high time delay when performing gradient calculation on real-time data. In this paper, we argue that the computational cost brought by fine-tuning can be rather unnecessary. We consequently present a novel perspective to improving DMG without increasing computational cost, i.e., device-specific parameter generation which directly maps data distribution to parameters. Specifically, we propose an efficient Device-cloUd collaborative parametErs generaTion framework DUET. DUET is deployed on a powerful cloud server that only requires the low cost of forwarding propagation and low time delay of data transmission between the device and the cloud. By doing so, DUET can rehearse the device-specific model weight realizations conditioned on the personalized real-time data for an individual device. Importantly, our DUET elegantly connects the cloud and device as a 'duet' collaboration, frees the DMG from fine-tuning, and enables a faster and more accurate DMG paradigm. We conduct an extensive experimental study of DUET on three public datasets, and the experimental results confirm our framework's effectiveness and generalisability for different DMG tasks.

翻译：设备模型泛化（DMG）是设备端机器学习应用中一个实用但尚未充分研究的课题。其目标是在资源受限设备上部署预训练模型时提升其泛化能力，例如提高预训练云模型在智能移动设备上的性能。尽管已有大量工作研究了云与设备间的数据分布偏移，但其中大多数聚焦于通过个性化数据对单个设备进行模型微调以促进DMG。尽管这些方法具有潜力，但它们需要设备端重新训练，而在实时数据上进行梯度计算时，由于过拟合问题和较高的时间延迟，这在实践中并不可行。本文认为，微调带来的计算成本在很大程度上是不必要的。因此，我们提出了一种无需增加计算成本即可改进DMG的新视角，即直接完成数据分布到参数的映射，实现设备特定的参数生成。具体而言，我们提出了一种高效的设备-云协同参数生成框架DUET。DUET部署在强大的云端服务器上，仅需前向传播的低成本以及设备与云之间数据传输的低延迟。通过这种方式，DUET能够针对单个设备的个性化实时数据，推演其设备特定的模型权重实例。重要的是，我们的DUET巧妙地将云与设备连接为“二重奏”协同，使DMG摆脱微调过程，并实现更快、更准确的DMG范式。我们在三个公开数据集上对DUET进行了广泛的实验研究，实验结果证实了该框架在不同DMG任务中的有效性和泛化能力。