dreaMLearning: Data Compression Assisted Machine Learning
This addresses efficiency issues for ML applications like distributed, federated, and edge computing, though it appears incremental as it builds on existing compression methods.
The paper tackles the problem of machine learning being hindered by large data and resource demands by introducing dreaMLearning, a framework that learns from compressed data without decompression, resulting in up to 8.8x faster training, 10x reduced memory usage, and 42% storage savings with minimal performance impact.
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.