martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture

The development of machine learning models requires a large amount of training data. Data marketplaces are essential for trading high-quality, private-domain data not publicly available online. However, due to growing data privacy concerns, direct data exchange is inappropriate. Federated Learning (FL) is a distributed machine learning paradigm that exchanges data utilities (in form of local models or gradients) among multiple parties without directly sharing the raw data. However, several challenges exist when applying existing FL architectures to construct a data …

architecture cs.cr data data privacy development distributed domain exchange federated federated learning high large machine machine learning machine learning models marketplace paradigm privacy privacy concerns private quality trading training training data utility