通过社交聚类选代表，减少联邦学习中的数据异质性影响
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions
- 按客户相似性分组，每组选一个代表性客户端
- 相比基线，准确率更高且收敛更快
- 适合数据差异大的联邦学习场景
联邦学习（FL）可在保护隐私的前提下实现协作模型训练，但其效果常受客户端数据异质性限制。本文提出一种动态客户选择算法：(i) 基于渐近一致性构建非重叠的客户联盟；(ii) 每个联盟中选出一个代表以最小化模型更新方差。该方法受社会网络建模启发，利用基于同质性的邻近矩阵进行谱聚类，并采用识别最具信息量个体的技术来估计群体共识。在标准联邦学习假设下，我们提供了算法的理论收敛性保证。最后，通过与三种强异质性感知基线对比验证，结果表明本方法在准确率和收敛速度上均更优，说明该框架兼具理论严谨性与实践有效性。
原文摘要 · Abstract (English)
Federated Learning (FL) enables privacy-preserving collaborative model training, but its effectiveness is often limited by client data heterogeneity. We introduce a client-selection algorithm that (i) dynamically forms nonoverlapping coalitions of clients based on asymptotic agreement and (ii) selects one representative from each coalition to minimize the variance of model updates. Our approach is inspired by social-network modeling, leveraging homophily-based proximity matrices for spectral clustering and techniques for identifying the most informative individuals to estimate a group's aggregate opinion. We provide theoretical convergence guarantees for the algorithm under mild, standard FL assumptions. Finally, we validate our approach by benchmarking it against three strong heterogeneity-aware baselines; the results show higher accuracy and faster convergence, indicating that the framework is both theoretically grounded and effective in practice.
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