FedSST：重新审视结构偏移下的公平联邦图学习
FedSST: Rethinking Fair Federated Graph Learning under Structural Shift
对做视觉图结构数据（如场景图、点云关系图、多相机拓扑）的联邦训练，提供了一种用结构信号量化客户端贡献、抑制聚合偏置的思路，可迁移到跨域视觉客户端的公平聚合与自适应本地训练。
原文依据 FedSST introduces a fair, structure-based signal to quantify client contributions, which in turn guides fair aggregation and adaptive local training.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论联邦图学习（FGL）为图数据上的协作训练提供了一种隐私保护范式，然而显著的拓扑异质性对泛化公平性构成严重威胁，往往产生由部分客户端主导的全局模型。这带来两个关键问题：在全局层面，聚合偏置会不成比例地放大主导客户端的影响；在本地层面，盲目优化导致训练过程低效且不公平。为应对这些挑战，我们提出 FedSST，一种自适应公平框架。FedSST 引入一种公平的、基于结构的信号来量化客户端贡献，进而指导公平聚合与自适应本地训练。在多种跨域与跨数据集设置下的大量实验表明，FedSST 提升了泛化公平性与整体模型性能，优于多种最优方法。
英文原文摘要 953 字符
Federated Graph Learning (FGL) offers a privacy-preserving paradigm for collaborative training on graph data, yet significant topological heterogeneity poses a critical threat to generalization fairness, often yielding a global model dominated by a subset of clients. This introduces two critical issues: at the global level, aggregation bias disproportionately amplifies the influence of dominant clients, while at the local level, blind optimization results in inefficient and inequitable training processes. To address these challenges, we propose FedSST, an adaptive fairness framework. FedSST introduces a fair, structure-based signal to quantify client contributions, which in turn guides fair aggregation and adaptive local training. Extensive experiments across diverse cross-domain and cross-dataset settings demonstrate that FedSST enhances generalization fairness and overall model performance, outperforming various state-of-the-art methods.