Towards Fair and Robust Graph Federated Learning via Incentive Mechanisms

Abstract

Graph federated learning (FL) allows multiple agents to collaboratively train a global graph model while preserving data privacy. However, current methods overlook the issue of low-quality or malicious data contributions, which can degrade the global model’s performance. This paper addresses this gap by proposing a novel incentive mechanism for graph FL, called GFLValue. We identify two main challenges in graph federated learning: agents causing harm and those contributing with delays. Our framework introduces an agent valuation function based on two criteria: gradient alignment and graph diversity. To balance accuracy and fairness, motif prototypes are used to enhance the global model aggregation and support local optimization. Additionally, we incorporate Gaussian noise into GFLValue to ensure Differential Privacy (DP), creating a privacy-preserving version, GFLValue+. In the case of malicious agents, we observe that local and global motif prototypes can distinguish between malicious and honest agents. This leads to the GFLValue-Adv framework, which integrates motif prototype distances and agent values to detect malicious behavior. Experiments show GFLValue offers the best trade-off between accuracy and fairness, while GFLValue+ enhances privacy without significantly compromising performance. GFLValue-Adv improves malicious agent detection accuracy by 11.7% and reduces training time by 6.94%.