Mechanism Design for Federated Learning with Non-Monotonic Network Effects

2026-01-08

arXiv.org

AI Summary

This work addresses the limitations of existing incentive mechanisms in federated learning, which overlook the non-monotonic network effects among clients and the heterogeneous model performance requirements of applications, leading to insufficient incentives and suboptimal social welfare. The study is the first to reveal the non-monotonic nature of network effects in federated learning and proposes a novel Model Trading and Sharing (MoTS) framework. Building upon this, it introduces an application-aware incentive mechanism—Social Welfare maximization under Application-specific requirements (SWAN)—that explicitly incorporates task-specific generalization error constraints into the incentive design. Grounded in mechanism design theory and validated through hardware prototype experiments, SWAN achieves up to a 352.42% improvement in social welfare while reducing excess incentive costs by 93.07%, substantially outperforming state-of-the-art approaches.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Distributed Machine Learning & Federated Learning
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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 $93.07\%$.

Problem

• Federated Learning
• Mechanism Design
• Network Effects
• Model Performance Requirements
• Social Welfare

Innovation

• Federated Learning
• Network Effects
• Mechanism Design
• Social Welfare
• Non-Monotonicity