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Federated learning (FL) is an emerging machine learning paradigm where multiple distributed clients collaboratively train a model without centrally collecting their raw data. In FL setting, it is a common case that the data on local clients come from different domains, e.g., photos taken by different mobile phones can vary in intensity and contrast due to the difference of imaging parameters. In such a cross-domain case, features extracted from data of different clients deviate from each other in the feature space, leading to the so-called feature shift. The feature shift can reduce the discrimination of features and degrade the performance of the learned model. However, most existing FL methods are not particularly designed for cross-domain setting. In this paper, we propose a novel cross-domain FL method, named AlignFed. In AlignFed, the model on each client is separated to a personalized feature extractor and a …