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: this https URL

在个性化联邦学习领域，当一个联邦模型已经训练和部署，并且有一个未标记的新客户端加入时，为新客户端提供个性化模型是一个极具挑战性的任务。本文针对这一挑战，将自适应风险最小化技术扩展到无监督个性化联邦学习设置中，并提出我们的方法 FedTTA。我们通过使用代理正则化和熵提前停止调整来改进 FedTTA，进而提出了两种简单而有效的优化策略。此外，我们还针对 FedTTA 设计了一种专门的知识蒸馏损失，以解决设备异构性问题。针对十一个基准测试，通过对五个数据集进行大量实验，证明了我们提出的 FedTTA 及其变种的有效性。