Mechanism design is pivotal to federated learning (FL) for maximizing social welfare by coordinating self-interested clients. Existing mechanisms, however, often overlook the network effects of client participation and the diverse model performance requirements (i.e., generalization error) across applications, leading to suboptimal incentives and social welfare, or even inapplicability in real deployments. To address this gap, we explore incentive mechanism design for FL with network effects and application-specific requirements of model performance. We develop a theoretical model to quantify the impact of network effects on heterogeneous client participation, revealing the non-monotonic nature of such effects. Based on these insights, we propose a Model Trading and Sharing (MoTS) framework, which enables clients to obtain FL models through either participation or purchase. To further address clients' strategic behaviors, we design a Social Welfare maximization with Application-aware and Network effects (SWAN) mechanism, exploiting model customer payments for incentivization. Experimental results on a hardware prototype demonstrate that our SWAN mechanism outperforms existing FL mechanisms, improving social welfare by up to $352.42\%$ and reducing extra incentive costs by $93.07\%$.

翻译：机制设计对于联邦学习（FL）至关重要，它通过协调自利的客户端以实现社会福利最大化。然而，现有机制往往忽视了客户端参与的网络效应以及不同应用对模型性能（即泛化误差）的多样化要求，导致激励措施和社会福利次优，甚至在实际部署中不适用。为弥补这一不足，我们探索了具有网络效应和特定应用模型性能要求的联邦学习激励机制设计。我们建立了一个理论模型来量化网络效应对异构客户端参与的影响，揭示了此类效应的非单调性。基于这些发现，我们提出了一个模型交易与共享（MoTS）框架，使客户端能够通过参与或购买两种方式获取联邦学习模型。为了进一步应对客户端的策略性行为，我们设计了一种具有应用感知和网络效应的社会福利最大化（SWAN）机制，利用模型客户端的支付进行激励。在硬件原型上的实验结果表明，我们的SWAN机制优于现有的联邦学习机制，将社会福利提高了高达$352.42\%$，并将额外激励成本降低了$93.07\%$。