P2U: Progressive Precision Update For Efficient Model Distribution

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 (P2U) to address this problem. Instead of transmitting the original high-precision model, P2U 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 P2U 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 P2U 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 P2U 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.