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 than current federated learning approaches.

本文提出了一种名为 FedGen 的通用联邦学习框架，它允许客户端以协作的方式在没有训练分布的先验知识的情况下，识别和区分虚假和不变特征，与目前的联邦学习方法相比，FedGen 能够实现显著更好的泛化效果。