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研究不足的课题。其目标在于提升预训练模型在资源受限设备（如智能手机）上部署时的泛化能力，例如改进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，但多数聚焦于利用个性化数据对单个设备进行模型微调以促进DMG。尽管这些方法前景可观，但它们需要进行设备端重训练，由于实时数据梯度计算带来的过拟合问题和高时延，这在实际中往往不可行。本文认为，微调带来的计算成本可能并非必要。因此，我们提出了一种在不增加计算成本的前提下提升DMG的新视角，即设备特定参数生成——直接将数据分布映射至参数。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部署于强大的云服务器，仅需设备与云之间数据传输的低成本前向传播与低时延。通过这种方式，DUET能够基于个性化实时数据为单个设备预演设备特定的模型权重实现。重要的是，我们的DUET优雅地将云与设备连接为“二重奏”式协作，使DMG摆脱微调束缚，并实现更快速、更精准的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，实验结果证实了我们的框架在不同DMG任务中的有效性和泛化能力。