Data Measurements for Decentralized Data Markets

Charles Lu, Mohammad Mohammadi Amiri, Ramesh Raskar
6/6/2024
cs.LGcs.IR

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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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Cite this paper

@article{lu2024data,
  title  = {Data Measurements for Decentralized Data Markets},
  author = {Charles Lu and Mohammad Mohammadi Amiri and Ramesh Raskar},
  year   = {2024},
  eprint = {2406.04257},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.04257v1}
}

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