A Dataset is Worth 1 MB

TL;DR

This paper introduces PLADA, a method that drastically reduces dataset transmission costs by transmitting only class labels for selected images from a large reference dataset, enabling efficient task transfer with minimal payload.

Contribution

The paper proposes a novel label-based data transmission method that eliminates pixel transfer and addresses distribution mismatch through dataset pruning.

Findings

Achieves task transfer with less than 1 MB payload.

Maintains high classification accuracy across diverse datasets.

Effectively filters reference datasets to relevant images for target tasks.

Abstract

A dataset server must often distribute the same large payload to many clients, incurring massive communication costs. Since clients frequently operate on diverse hardware and software frameworks, transmitting a pre-trained model is often infeasible; instead, agents require raw data to train their own task-specific models locally. While dataset distillation attempts to compress training signals, current methods struggle to scale to high-resolution data and rarely achieve sufficiently small files. In this paper, we propose Pseudo-Labels as Data (PLADA), a method that completely eliminates pixel transmission. We assume agents are preloaded with a large, generic, unlabeled reference dataset (e.g., ImageNet-1K, ImageNet-21K) and communicate a new task by transmitting only the class labels for specific images. To address the distribution mismatch between the reference and target datasets, we introduce a pruning mechanism that filters the reference dataset to retain only the labels of the most semantically relevant images for the target task. This selection process simultaneously maximizes training efficiency and minimizes transmission payload. Experiments on 10 diverse datasets demonstrate that our approach can transfer task knowledge with a payload of less than 1 MB while retaining high classification accuracy, offering a promising solution for efficient dataset serving.