Federated Learning (FL) aims to foster collaboration among a population of clients to improve the accuracy of machine learning without directly sharing local data. Although there has been rich literature on designing federated learning algorithms, most prior works implicitly assume that all clients are willing to participate in a FL scheme. In practice, clients may not benefit from joining in FL, especially in light of potential costs related to issues such as privacy and computation. In this work, we study the clients' incentives in federated learning to help the service provider design better solutions and ensure clients make better decisions. We are the first to model clients' behaviors in FL as a network effects game, where each client's benefit depends on other clients who also join the network. Using this setup we analyze the dynamics of clients' participation and characterize the equilibrium, where no client has incentives to alter their decision. Specifically, we show that dynamics in the population naturally converge to equilibrium without needing explicit interventions. Finally, we provide a cost-efficient payment scheme that incentivizes clients to reach a desired equilibrium when the initial network is empty.

翻译：联邦学习旨在促进客户群体间的协作，以提升机器学习模型精度，同时避免直接共享本地数据。尽管已有大量关于联邦学习算法设计的文献，但先前的研究大多隐含假设所有客户都愿意参与联邦学习方案。实际上，客户可能无法从加入联邦学习中获益，尤其是考虑到隐私与计算等潜在成本问题。本研究通过分析联邦学习中客户的激励机制，帮助服务提供商设计更优解决方案，并确保客户做出更明智的决策。我们首次将联邦学习中客户的行为建模为网络效应博弈——在该博弈中，每个客户的收益取决于其他加入网络的客户。基于这一框架，我们分析了客户参与动态，并刻画了均衡状态（即任何客户均无动机改变其决策）。具体而言，研究表明群体动态无需外部干预即可自然收敛至均衡。最后，我们提出了一种成本高效的支付方案，该方案能在初始网络为空时激励客户达到理想均衡状态。