Data Measurements for Decentralized Data Markets

Charles Lu, Mohammad Mohammadi Amiri, Ramesh Raskar

Keywords: data valuation, data measurements, data markets

TL;DR: The paper presents a framework for federated data measurements in decentralized markets, enabling efficient buyer-seller matching without direct data access for data acquisition.

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.

Submission Number: 676