Title: dreaMLearning: Data Compression Assisted Machine Learning
Primary area: infrastructure, software libraries, hardware, systems, etc.
Keywords: Data-efficient learning, data compression

Abstract: 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 $10\times$, reduces peak memory usage of training data by $10\times$, and cuts storage by 37\%, 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.

TL;DR: dreaMLearning enables efficient machine learning by training directly on compressed data without decompression, achieving significant speed, memory, and storage gains with minimal accuracy loss.