用压缩数据直接训练模型，提速8.8倍且省10倍内存。
dreaMLearning: Data Compression Assisted Machine Learning
- 在不解压的前提下，直接从压缩数据学习，利用熵驱动去重技术
- 训练速度提升8.8倍，内存占用降低10倍，存储减少42%
- 适合边缘设备、联邦学习等资源受限场景
尽管机器学习尤其是深度学习取得了快速发展，但仍面临大量标注数据需求、过拟合风险以及巨大的计算与存储压力。本文提出dreaMLearning框架，支持在无需解压的情况下直接从压缩数据中学习，其基础是基于熵的广义去重（EntroGeDe）——一种将信息浓缩为少量代表性样本的熵驱动无损压缩方法。该框架可适配多种数据类型、任务及模型架构。在表格与图像数据上的回归与分类任务中，实验表明dreaMLearning可实现最高8.8倍的训练加速，内存使用减少10倍，存储需求降低42%，同时对模型性能影响极小。该技术显著提升了分布式学习、联邦学习及资源受限边缘设备上的tinyML应用效率，推动高效可扩展学习的新可能。
原文摘要 · Abstract (English)
Despite rapid advancements, machine learning, particularly deep learning, is hindered by the need for large amounts of labeled data to learn meaningful patterns without overfitting and immense demands for computation and storage, which motivate research into architectures that can achieve good performance with fewer resources. This paper introduces dreaMLearning, a novel framework that enables learning from compressed data without decompression, built upon Entropy-based Generalized Deduplication (EntroGeDe), an entropy-driven lossless compression method that consolidates information into a compact set of representative samples. DreaMLearning accommodates a wide range of data types, tasks, and model architectures. Extensive experiments on regression and classification tasks with tabular and image data demonstrate that dreaMLearning accelerates training by up to 8.8x, reduces memory usage by 10x, and cuts storage by 42%, with a minimal impact on model performance. These advancements enhance diverse ML applications, including distributed and federated learning, and tinyML on resource-constrained edge devices, unlocking new possibilities for efficient and scalable learning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。