这篇论文用CRF替代固定加权规则，解决了客户端数据分布不均的问题，在非IID场景下比经典联邦学习方法更稳定。
该论文提出了一种基于条件随机场(CRF)的联邦学习聚合权重优化框架。它通过定义客户端的一元势函数和客户端对的成对势函数，建模单个客户端的可靠性以及客户端间的交互。CRF推理生成的自适应聚合权重改进了全局训练目标的收敛性。在非IID数据异质性场景下，该方法在多个联邦学习基线方法上持续提升了性能。
Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneous data and varying contributions to the global model. We propose a framework that refines client aggregation weights using Conditional Random Fields (CRFs). Our method defines unary potentials for individual clients and pairwise potentials for all client pairs, allowing the server to model both client-specific reliability and interactions between clients. The resulting CRF inference produces aggregation weights that enable better convergence of the global training objective. Experiments show that, under non-IID heterogeneity, our approach consistently improves performance over well-established federated learning baselines.