Federated learning enables multiple actors to collaboratively train models without sharing private data. This unlocks the potential for scaling machine learning to diverse applications. Existing algorithms for this task are well-justified when clients and the intended target domain share the same distribution of features and labels, but this assumption is often violated in real-world scenarios. One common violation is label shift, where the label distributions differ across clients or between clients and the target domain, which can significantly degrade model performance. To address this problem, we propose FedPALS, a novel model aggregation scheme that adapts to label shifts by leveraging knowledge of the target label distribution at the central server. Our approach ensures unbiased updates under stochastic gradient descent, ensuring robust generalization across clients with diverse, label-shifted data. Extensive experiments on image classification demonstrate that FedPALS consistently outperforms standard baselines by aligning model aggregation with the target domain. Our findings reveal that conventional federated learning methods suffer severely in cases of extreme client sparsity, highlighting the critical need for target-aware aggregation. FedPALS offers a principled and practical solution to mitigate label distribution mismatch, ensuring models trained in federated settings can generalize effectively to label-shifted target domains.

翻译：联邦学习允许多个参与方在不共享私有数据的情况下协作训练模型，这为将机器学习扩展到多样化应用场景提供了可能。现有算法在客户端与目标域共享相同特征和标签分布时具有充分的理论依据，但这一假设在现实场景中常被违背。其中一种常见的违背情况是标签偏移，即不同客户端之间或客户端与目标域之间的标签分布存在差异，这会显著降低模型性能。为解决此问题，我们提出FedPALS——一种新颖的模型聚合方案，该方法通过利用中央服务器端的目标标签分布知识来适应标签偏移。我们的方法确保了随机梯度下降下的无偏更新，从而在具有多样化标签偏移数据的客户端间实现稳健的泛化能力。在图像分类任务上的大量实验表明，FedPALS通过将模型聚合与目标域对齐，持续优于标准基线方法。我们的研究揭示，传统联邦学习方法在客户端极度稀疏的情况下性能严重下降，这凸显了目标感知聚合的迫切需求。FedPALS为解决标签分布失配问题提供了原则性且实用的解决方案，确保在联邦学习环境下训练的模型能够有效泛化至存在标签偏移的目标域。