标题： P$^2$U：高效模型分发的渐进精度更新 显示英文标题

标题： P$^2$U: Progressive Precision Update For Efficient Model Distribution

摘要： 在带宽受限的环境中，高效的模型分发变得越来越关键。 在本文中，我们提出了一种简单而有效的方法，称为渐进精度更新（P$^2$U），以解决这个问题。 而不是传输原始的高精度模型，P$^2$U传输一个低比特精度的模型，同时传输一个表示原始高精度模型与传输的低精度版本之间差异的模型更新。 通过对各种模型架构的广泛实验，从小型模型（$1 - 6$百万参数）到大型模型（超过$100$百万参数），并使用三个不同的数据集，例如胸部X光、PASCAL-VOC和CIFAR-100，我们证明了P$^2$U在准确性、带宽使用和延迟之间始终能实现更好的权衡。 此外，我们表明当带宽或启动时间是优先考虑的因素时，可以使用激进的量化（例如4位）而不会严重损害性能。 这些结果确立了P$^2$U作为在低资源环境下可扩展且高效的模型分发的有效且实用的解决方案，包括联邦学习、边缘计算和物联网部署。 鉴于P$^2$U补充了现有的压缩技术，并且可以与任何压缩方法一起实现，例如稀疏化、量化、剪枝等，改进的潜力甚至更大。 显示英文摘要

摘要： Efficient model distribution is becoming increasingly critical in bandwidth-constrained environments. In this paper, we propose a simple yet effective approach called Progressive Precision Update (P$^2$U) to address this problem. Instead of transmitting the original high-precision model, P$^2$U transmits a lower-bit precision model, coupled with a model update representing the difference between the original high-precision model and the transmitted low precision version. With extensive experiments on various model architectures, ranging from small models ($1 - 6$ million parameters) to a large model (more than $100$ million parameters) and using three different data sets, e.g., chest X-Ray, PASCAL-VOC, and CIFAR-100, we demonstrate that P$^2$U consistently achieves better tradeoff between accuracy, bandwidth usage and latency. Moreover, we show that when bandwidth or startup time is the priority, aggressive quantization (e.g., 4-bit) can be used without severely compromising performance. These results establish P$^2$U as an effective and practical solution for scalable and efficient model distribution in low-resource settings, including federated learning, edge computing, and IoT deployments. Given that P$^2$U complements existing compression techniques and can be implemented alongside any compression method, e.g., sparsification, quantization, pruning, etc., the potential for improvement is even greater.