Federated learning (FL) allows distributed clients to collaboratively train a global model in a privacy-preserving manner. However, one major challenge is domain skew, where clients' data originating from diverse domains may hinder the aggregated global model from learning a consistent representation space, resulting in poor generalizable ability in multiple domains. In this paper, we argue that the domain skew is reflected in the domain-specific biased features of each client, causing the local model's representations to collapse into a narrow low-dimensional subspace. We then propose Federated Feature Decoupling and Calibration ($F^2$DC), which liberates valuable class-relevant information by calibrating the domain-specific biased features, enabling more consistent representations across domains. A novel component, Domain Feature Decoupler (DFD), is first introduced in $F^2$DC to determine the robustness of each feature unit, thereby separating the local features into domain-robust features and domain-related features. A Domain Feature Corrector (DFC) is further proposed to calibrate these domain-related features by explicitly linking discriminative signals, capturing additional class-relevant clues that complement the domain-robust features. Finally, a domain-aware aggregation of the local models is performed to promote consensus among clients. Empirical results on three popular multi-domain datasets demonstrate the effectiveness of the proposed $F^2$DC and the contributions of its two modules. Code is available at https://github.com/mala-lab/F2DC.

翻译：联邦学习（FL）允许分布式客户端以保护隐私的方式协作训练全局模型。然而，其主要挑战之一是领域倾斜，即客户端数据源自不同领域可能导致聚合后的全局模型难以学习到一致的表示空间，从而在多领域场景下泛化能力较差。本文指出，领域倾斜体现在每个客户端特定领域的偏置特征上，导致局部模型的表示坍缩至狭窄的低维子空间。为此，我们提出联邦特征解耦与校准方法（$F^2$DC），通过校准特定领域的偏置特征，释放有价值的类别相关信息，从而在不同领域间实现更一致的表示。$F^2$DC首先引入新型组件领域特征解耦器（DFD），用于确定每个特征单元的鲁棒性，从而将局部特征分离为领域鲁棒特征和领域相关特征。进一步提出领域特征校正器（DFC），通过显式关联判别性信号来校准这些领域相关特征，捕获补充领域鲁棒特征的额外类别相关线索。最后，对局部模型执行领域感知聚合以促进客户端间的共识。在三个主流多领域数据集上的实验结果验证了所提$F^2$DC方法及其两个模块的有效性。代码已开源：https://github.com/mala-lab/F2DC。