  Personalized federated learning has gained significant attention as a
promising approach to address the challenge of data heterogeneity. In this
paper, we address a relatively unexplored problem in federated learning. When a
federated model has been trained and deployed, and an unlabeled new client
joins, providing a personalized model for the new client becomes a highly
challenging task. To address this challenge, we extend the adaptive risk
minimization technique into the unsupervised personalized federated learning
setting and propose our method, FedTTA. We further improve FedTTA with two
simple yet effective optimization strategies: enhancing the training of the
adaptation model with proxy regularization and early-stopping the adaptation
through entropy. Moreover, we propose a knowledge distillation loss
specifically designed for FedTTA to address the device heterogeneity. Extensive
experiments on five datasets against eleven baselines demonstrate the
effectiveness of our proposed FedTTA and its variants. The code is available
at: https://github.com/anonymous-federated-learning/code.
