Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current efforts overlook a key issue: agents are self-interested and would hesitant to share data without fair and satisfactory incentives. This paper is the first endeavor to address this issue by studying the incentive mechanism for graph federated learning. We identify a unique phenomenon in graph federated learning: the presence of agents posing potential harm to the federation and agents contributing with delays. This stands in contrast to previous FL incentive mechanisms that assume all agents contribute positively and in a timely manner. In view of this, this paper presents a novel incentive mechanism tailored for fair graph federated learning, integrating incentives derived from both model gradient and payoff. To achieve this, we first introduce an agent valuation function aimed at quantifying agent contributions through the introduction of two criteria: gradient alignment and graph diversity. Moreover, due to the high heterogeneity in graph federated learning, striking a balance between accuracy and fairness becomes particularly crucial. We introduce motif prototypes to enhance accuracy, communicated between the server and agents, enhancing global model aggregation and aiding agents in local model optimization. Extensive experiments show that our model achieves the best trade-off between accuracy and the fairness of model gradient, as well as superior payoff fairness.

翻译：图联邦学习作为一种关键范式，使多个智能体能够在保护本地数据隐私的同时协作训练图模型。然而，现有研究忽视了关键问题：智能体具有自利性，若无公平且令人满意的激励，便会犹豫是否共享数据。本文首次通过研究图联邦学习的激励机制来解决这一问题。我们发现了图联邦学习中的独特现象：存在对联邦造成潜在危害的智能体以及延迟贡献的智能体。这不同于以往假设所有智能体均及时做出正向贡献的联邦学习激励机制。基于此，本文提出了一种面向公平图联邦学习的新型激励机制，整合了来自模型梯度和收益两方面的激励。为实现该目标，我们首先引入智能体价值函数，通过梯度对齐与图多样性两个准则量化智能体贡献。此外，针对图联邦学习中存在的高度异质性，平衡准确性与公平性变得尤为关键。我们引入基序原型（motif prototypes）以提升准确性，该原型在服务器与智能体间传递，可增强全局模型聚合并辅助智能体优化本地模型。大量实验表明，本模型在模型梯度的准确性与公平性之间实现了最佳权衡，并展现出卓越的收益公平性。