Data Acquisition via Experimental Design for Decentralized Data Markets

arXiv:2403.13893v1 Announce Type: new
Abstract: Acquiring high-quality training data is essential for current machine learning models. Data markets provide a way to increase the supply of data, particularly in data-scarce domains such as healthcare, by incentivizing potential data sellers to join the market. A major challenge for a data buyer in such a market is selecting the most valuable data points from a data seller. Unlike prior work in data valuation, which assumes centralized data access, we propose a federated …

abstract acquisition arxiv challenge cs.lg current data decentralized decentralized data design domains experimental healthcare join machine machine learning machine learning models major market markets quality sellers training training data type via