Data Measurements for Decentralized Data Markets
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866914827767644160 |
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| author | Lu, Charles Amiri, Mohammad Mohammadi Raskar, Ramesh |
| author_facet | Lu, Charles Amiri, Mohammad Mohammadi Raskar, Ramesh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04257 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Measurements for Decentralized Data Markets Lu, Charles Amiri, Mohammad Mohammadi Raskar, Ramesh Machine Learning Information Retrieval 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. |
| title | Data Measurements for Decentralized Data Markets |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2406.04257 |