Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients. One such approach in deep neural networks based tasks is employing a shared feature representation and learning a customized classifier head for each client. However, previous works do not utilize the global knowledge during local representation learning and also neglect the fine-grained collaboration between local classifier heads, which limit the model generalization ability. In this work, we conduct explicit local-global feature alignment by leveraging global semantic knowledge for learning a better representation. Moreover, we quantify the benefit of classifier combination for each client as a function of the combining weights and derive an optimization problem for estimating optimal weights. Finally, extensive evaluation results on benchmark datasets with various heterogeneous data scenarios demonstrate the effectiveness of our proposed method. Code is available at https://github.com/JianXu95/FedPAC

翻译：数据异质性是联邦学习中最具挑战性的问题之一，这促使了多种为参与客户端学习个性化模型的方法。在基于深度神经网络的任务中，一种常见方法是共享特征表示，并为每个客户端学习定制化的分类器头。然而，以往的工作在局部表示学习过程中未利用全局知识，也忽略了各局部分类器头之间的细粒度协作，这限制了模型的泛化能力。在本工作中，我们通过利用全局语义知识进行显式的局部-全局特征对齐，以学习更好的表示。此外，我们量化了每个客户端分类器组合的收益与组合权重之间的函数关系，并推导出一个优化问题以估计最优权重。最后，在具有多种异质数据场景的基准数据集上的大量评估结果，证明了我们提出的方法的有效性。代码地址：https://github.com/JianXu95/FedPAC