mlsanityData to 7 Oct 2026

Data Measurements for Decentralized Data Markets

arXiv 2406.04257 ↗First posted 6 Jun 2024cs.LG cs.IRcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 71 references that are in the map. First posted on arXiv 6 Jun 2024.

Builds on

28 of 71 references.

References · 28

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16 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Private, Augmentation-Robust and Task-Agnostic Data Valuation Approach for Data Marketplacecited by 1
  2. 2026Efficient Decentralized Multi-task Dataset Valuation via Model Mergingcited by 0