Abstract: Given the excellent performance of contrastive learning on downstream tasks, contrastive clustering has received much more attention recently. However, most approaches only utilize a simple kind of data augmentation. Although augmented views keep the majority of information from original samples, they also inherit a mixture of characteristic of features, including semantic and non-semantic features, which limits model’s learning ability of semantic information under similar or identical view patterns. Even some approaches regard two different augmentation views being from the same sample and keeping similar view patterns as positive pairs, which results in sample pairs lacking of semantics. In this paper, we propose a semantic contrastive clustering method with federated data augmentation to solve these problems. Two different types of data augmentations, namely strong data augmentation and weak data augmentation, are introduced to produce two very different view patterns. These two view patterns are utilized to mitigate the disturbance of non-semantic information and improve the semantic awareness of the proposed approach. Moreover, a global k-nearest neighbor graph is used to bring global category information, which instructs the model to treat different samples from the same cluster as positive pairs. Extensive experiments on six commonly used and challenging image datasets show that the proposed method achieves the state-of-the-art performance and confirms the superiority and validity of it.