Federated Learning and Free-riding in a Competitive Market
Jiajun Meng, Jing Chen, Dongfang Zhao, Lin Liu

TL;DR
This paper models how free-riding impacts firms' incentives to form federated learning in competitive markets, revealing conditions under which FL is beneficial or discouraged, and analyzing subsidy effects.
Contribution
It introduces a game-theoretic model analyzing free-riding effects on federated learning formation among competing firms, highlighting new strategic insights.
Findings
Firms with more information always contribute in FL despite free-riding.
Less-informed firms tend to free ride, influencing FL formation incentives.
An 'All-Win' scenario exists where all stakeholders benefit from FL.
Abstract
Federated learning (FL) is a collaborative technique for training large-scale models while protecting user data privacy. Despite its substantial benefits, the free-riding behavior raises a major challenge for the formation of FL, especially in competitive markets. Our paper explores this under-explored issue on how the free-riding behavior in a competitive market affects firms' incentives to form FL. Competing firms can improve technologies through forming FL to increase the performance of their products, which in turn, affects consumers' product selection and market size. The key complication is whether the free-riding behavior discourages information contribution by participating firms and results in the decomposition of FL, and even free riding does not discourage information contribution, this does not necessarily mean that a firm wants to form FL in a competitive market because…
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Taxonomy
TopicsAuction Theory and Applications · Blockchain Technology Applications and Security · Digital Platforms and Economics
