In modern federated learning, one of the main challenges is to account for inherent heterogeneity and the diverse nature of data distributions for different clients. This problem is often addressed by introducing personalization of the models towards the data distribution of the particular client. However, a personalized model might be unreliable when applied to the data that is not typical for this client. Eventually, it may perform worse for these data than the non-personalized global model trained in a federated way on the data from all the clients. This paper presents a new approach to federated learning that allows selecting a model from global and personalized ones that would perform better for a particular input point. It is achieved through a careful modeling of predictive uncertainties that helps to detect local and global in- and out-of-distribution data and use this information to select the model that is confident in a prediction. The comprehensive experimental evaluation on the popular real-world image datasets shows the superior performance of the model in the presence of out-of-distribution data while performing on par with state-of-the-art personalized federated learning algorithms in the standard scenarios.

翻译：在现代联邦学习中，一个主要挑战是如何处理不同客户端数据分布的内在异质性和多样性。这一问题通常通过引入针对特定客户端数据分布的模型个性化来解决。然而，当个性化模型应用于该客户端非典型数据时，可能变得不可靠。最终，对于这些数据，其性能可能不如基于所有客户端数据以联邦方式训练的非个性化全局模型。本文提出一种新的联邦学习方法，允许从全局和个性化模型中选择对特定输入点性能更优的模型。这一目标通过精细建模预测不确定性实现，该方法有助于检测局部和全局的分布内与分布外数据，并利用这些信息选择对预测结果置信度较高的模型。在主流真实图像数据集上的综合实验评估表明，该模型在存在分布外数据时表现出卓越性能，同时在标准场景下与最先进的个性化联邦学习算法性能相当。