Towards Fair Graph Federated Learning via Incentive Mechanisms
Chenglu Pan Jiarong Xu Yue Yu Ziqi Yang Qingbiao Wu Chunping Wang Lei Chen Yang Yang
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.