Data Measurements for Decentralized Data Markets
Machine Learning
2024-06-07 v1 Information Retrieval
Abstract
Decentralized data markets can provide more equitable forms of data acquisition for machine learning. However, to realize practical marketplaces, efficient techniques for seller selection need to be developed. We propose and benchmark federated data measurements to allow a data buyer to find sellers with relevant and diverse datasets. Diversity and relevance measures enable a buyer to make relative comparisons between sellers without requiring intermediate brokers and training task-dependent models.
Keywords
Cite
@article{arxiv.2406.04257,
title = {Data Measurements for Decentralized Data Markets},
author = {Charles Lu and Mohammad Mohammadi Amiri and Ramesh Raskar},
journal= {arXiv preprint arXiv:2406.04257},
year = {2024}
}
Comments
20 pages, 11 figures