设计新型激励机制，让参与联邦学习的客户端更高效协作。
Mechanism Design for Federated Learning with Non-Monotonic Network Effects
- 提出模型交易与共享框架，支持参与或付费获取模型。
- 实测社会福利提升352.42%，激励成本降低93.07%。
- 适用于对模型性能有差异需求的现实场景，如医疗、金融。
机制设计在联邦学习中至关重要，可协调自利客户端以最大化社会福利。然而，现有机制常忽略客户端参与带来的网络效应及不同应用场景对模型性能（如泛化误差）的差异化需求，导致激励不足、社会福利低下，甚至难以实际部署。为此，本文研究考虑网络效应和应用特定性能要求的联邦学习激励机制设计。我们构建理论模型，量化网络效应对异构客户端参与的影响，揭示其非单调特性。基于此，提出模型交易与共享（MoTS）框架，使客户端可通过参与或购买获得联邦学习模型。为进一步应对客户端策略行为，设计了兼顾应用感知与网络效应的社会福利最大化机制（SWAN），利用模型客户支付实现激励。在硬件原型上的实验表明，所提SWAN机制优于现有联邦学习机制，社会福利最高提升352.42%，额外激励成本降低93.07%。
原文摘要 · Abstract (English)
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 $93.07\%$.
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