FedReLa不用知道全局分布就能重新标注数据，专治联邦学习里数据不平衡和异构的混合难题，少数类准确率提升明显。
FedReLa是一种数据层方法，解决联邦学习中全局类不平衡与客户端间数据异质性的共存问题。它采用特征依赖的标签重新分配器，无需全局类分布知识即可纠正有偏的全局决策边界。该方法模块化、模型无关，能与算法方法集成而不增加通信开销。在stepwise-imbalanced和long-tailed数据集上的实验显示，FedReLa显著提升了少数类准确率与整体准确率，超越先前最优方法。
FedReLa: Imbalanced Federated Learning via Re-Labeling
Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation. The global class imbalance and cross-client data heterogeneity naturally coexist, and the mismatch between local and global imbalances exacerbates the performance degradation of the aggregated model. The agnosticism of global class distribution poses significant challenges for data-level methods, especially under extreme conditions with severe class absence across clients. In this paper, we propose FedReLa, a novel data-level approach that tackles the coexistence of data heterogeneity and class imbalance in federated learning. By re-labeling samples with a feature-dependent label re-allocator, FedReLa corrects biased global decision boundaries without requiring knowledge of the global class distribution. This modular, model-agnostic approach can be integrated with algorithmic methods to deliver consistent improvements without additional communication overhead. Through extensive experiments, our method significantly improves the accuracy of minority classes and the overall accuracy on stepwise-imbalanced and long-tailed datasets, outperforming the previous state of the art.