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

2025-06-28

AI Summary

To address the challenge of model distribution under bandwidth-constrained settings, this paper proposes a progressive precision update method. It transmits ultra-low-bit (down to 4-bit) quantized models alongside lightweight precision compensation deltas, significantly reducing communication overhead while preserving model accuracy. The method introduces a novel differential update mechanism that enables aggressive quantization and stable precision recovery, and is compatible with mainstream compression techniques such as sparsification and pruning. By integrating low-bit quantization with delta encoding, it achieves multi-stage, gradual precision restoration. Extensive experiments across diverse model architectures and datasets demonstrate superior trade-offs among accuracy, bandwidth consumption, and latency. Compared to conventional approaches, the proposed method reduces communication volume substantially—particularly benefiting bandwidth- and resource-constrained scenarios such as federated learning and edge computing.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Diffusion Models for Vision
• Data Mining & Knowledge Management: Data Compression

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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.

Problem

• Efficient model distribution in bandwidth-constrained environments
• Tradeoff between accuracy, bandwidth usage and latency
• Scalable model distribution for low-resource settings

Innovation

• Progressive Precision Update for model distribution
• Transmits low-bit precision model with updates
• Balances accuracy, bandwidth, and latency effectively