Data Measurements for Decentralized Data Markets

• URL: http://arxiv.org/abs/2406.04257v1
• Date: Thu, 6 Jun 2024 17:03:51 GMT
• Title: Data Measurements for Decentralized Data Markets
• Authors: Charles Lu, Mohammad Mohammadi Amiri, Ramesh Raskar,
• Abstract summary: Decentralized data markets can provide more equitable forms of data acquisition for machine learning. We propose and benchmark federated data measurements to allow a data buyer to find sellers with relevant and diverse datasets.
• Score: 18.99870296998749
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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