Title: Towards Addressing Label Skews in One-Shot Federated Learning. Abstract: Federated learning (FL) has been a popular research area, where multiple clients collaboratively train a model without sharing their local raw data. Among existing FL solutions, one-shot FL is a promising and challenging direction, where the clients conduct FL training with a single communication round. However, while label skew is a common real-world scenario where some clients may have few or no data of some classes, existing one-shot FL approaches that conduct voting on the local models are not able to produce effective global models. Due to the limited number of classes in each party, the local models misclassify the data from unseen classes into seen classes, which leads to very ineffective global models from voting. To address the label skew issue in one-shot FL, we propose a novel approach named FedOV which generates diverse outliers and introduces them as an additional unknown class in local training to improve the voting performance. Specifically, based on open-set recognition, we propose novel outlier generation approaches by corrupting the original features and further develop adversarial learning to enhance the outliers. Our extensive experiments show that FedOV can significantly improve the test accuracy compared to state-of-the-art approaches in various label skew settings.