Rewarding High-Quality Data via Influence Functions
Abstract
We consider a crowdsourcing data acquisition scenario, such as federated learning, where a Center collects data points from a set of rational Agents, with the aim of training a model. For linear regression models, we show how a payment structure can be designed to incentivize the agents to provide high-quality data as early as possible, based on a characterization of the influence that data points have on the loss function of the model. Our contributions can be summarized as follows: (a) we prove theoretically that this scheme ensures truthful data reporting as a game-theoretic equilibrium and further demonstrate its robustness against mixtures of truthful and heuristic data reports, (b) we design a procedure according to which the influence computation can be efficiently approximated and processed sequentially in batches over time, (c) we develop a theory that allows correcting the difference between the influence and the overall change in loss and (d) we evaluate our approach on real datasets, confirming our theoretical findings.
Cite
@article{arxiv.1908.11598,
title = {Rewarding High-Quality Data via Influence Functions},
author = {Adam Richardson and Aris Filos-Ratsikas and Boi Faltings},
journal= {arXiv preprint arXiv:1908.11598},
year = {2019}
}