解决联邦图学习中因数据重叠不均导致的不公平问题
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
- 通过可解释加权聚合提升跨客户端公平性
- 在4个基准数据集上同时提升模型性能与公平性
- 适合关注隐私保护下公平性的图学习研究者
图联邦学习可在保护原始数据隐私的同时，协同提取分布式子图中的高阶信息。然而，图数据常在不同客户端间存在重叠。已有研究指出重叠数据有助于缓解数据异质性，但未探讨其不平衡带来的负面影响。本文通过实证观察与理论分析，揭示了不平衡重叠子图引发的不公平问题。为此，提出FairGFL（公平感知子图联邦学习）算法，在保障隐私的前提下增强跨客户端公平性。该方法采用可解释的加权聚合策略，结合对各客户端重叠比例的隐私保护估计；并通过在联邦复合损失函数中引入精心设计的正则项，优化模型效用与公平性的权衡。在四个基准图数据集上的大量实验表明，FairGFL在模型效用和公平性方面均优于四种代表性基线算法。
原文摘要 · Abstract (English)
Graph federated learning enables the collaborative extraction of high-order information from distributed subgraphs while preserving the privacy of raw data. However, graph data often exhibits overlap among different clients. Previous research has demonstrated certain benefits of overlapping data in mitigating data heterogeneity. However, the negative effects have not been explored, particularly in cases where the overlaps are imbalanced across clients. In this paper, we uncover the unfairness issue arising from imbalanced overlapping subgraphs through both empirical observations and theoretical reasoning. To address this issue, we propose FairGFL (FAIRness-aware subGraph Federated Learning), a novel algorithm that enhances cross-client fairness while maintaining model utility in a privacy-preserving manner. Specifically, FairGFL incorporates an interpretable weighted aggregation approach to enhance fairness across clients, leveraging privacy-preserving estimation of their overlapping ratios. Furthermore, FairGFL improves the tradeoff between model utility and fairness by integrating a carefully crafted regularizer into the federated composite loss function. Through extensive experiments on four benchmark graph datasets, we demonstrate that FairGFL outperforms four representative baseline algorithms in terms of both model utility and fairness.
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