Federated Learning (FL) is a machine learning paradigm where many clients collaboratively
learn a shared global model with decentralized training data. Personalized FL additionally
adapts the global model to different clients, achieving promising results on consistent local
training and test distributions. However, for real-world personalized FL applications, it is
crucial to go one step further: robustifying FL models under the evolving local test set during
deployment, where various distribution shifts can arise. In this work, we identify the pitfalls of …