When Federated Learning Meets Oligopoly Competition: Stability and Model Differentiation

Federated learning (FL) is decentralized machine learning framework that finds various applications in health, finance, and the internet of things. This paper studies the under-explored business competition in FL, where organizations are both collaborators in training a shared model and competitors in providing model-based services to a continuum of customers. We focus on an oligopoly case with three organizations. To understand how competition affects FL collaboration, we start with a benchmark case where organizations are not competitors, and show that they have an incentive to collaborate. However, in the presence of competition, organizations may prefer to train local models instead of collaborating via FL (even if FL incurs zero training costs). The reason is that FL intensifies price competition by improving organizations' model performance to a similar level. To address this issue, we devise a model differentiation mechanism in which organizations adaptively adjust their model performance, enabling differentiated model-based services to customers. We prove that the adaptive mechanism converges in polynomial time and is incentive compatible. Perhaps surprisingly, numerical experiments on CIFAR-10 show that the mechanism can simultaneously improve the model performance, organizations' revenues, and social welfare. The improvement is up to 22.31%, 14.42%, and 19.50%, respectively.