Existing federated learning models that follow the standard risk minimization paradigm of machine learning often fail to generalize in the presence of spurious correlations in the training data. In many real-world distributed settings, spurious correlations exist due to biases and data sampling issues on distributed devices or clients that can erroneously influence models. Current generalization approaches are designed for centralized training and attempt to identify features that have an invariant causal relationship with the target, thereby reducing the effect of spurious features. However, such invariant risk minimization approaches rely on apriori knowledge of training data distributions which is hard to obtain in many applications. In this work, we present a generalizable federated learning framework called FedGen, which allows clients to identify and distinguish between spurious and invariant features in a collaborative manner without prior knowledge of training distributions. We evaluate our approach on real-world datasets from different domains and show that FedGen results in models that achieve significantly better generalization and can outperform the accuracy of current federated learning approaches by over 24%.

翻译：现有的遵循机器学习标准风险最小化范式的联邦学习模型，在训练数据存在虚假相关时往往无法泛化。在许多实际分布式场景中，由于分布式设备或客户端上的偏差和数据采样问题，虚假相关会存在并错误地影响模型。当前的泛化方法专为集中式训练设计，试图识别与目标具有不变因果关系的特征，从而减少虚假特征的影响。然而，此类不变风险最小化方法依赖于训练数据分布的先验知识，这在许多应用中难以获得。在本工作中，我们提出了一种名为FedGen的可泛化联邦学习框架，该框架允许客户端在无需训练分布先验知识的情况下，以协作方式识别并区分虚假特征与不变特征。我们在不同领域的真实数据集上评估了该方法，结果表明FedGen生成的模型具有显著的泛化能力提升，其准确率可超过当前联邦学习方法24%以上。