Federated learning enables multiple clients, such as mobile phones and organizations, to collaboratively learn a shared model for prediction while protecting local data privacy. However, most recent research and applications of federated learning assume that all clients have fully labeled data, which is impractical in real-world settings. In this work, we focus on a new scenario for cross-silo federated learning, where data samples of each client are partially labeled. We borrow ideas from semi-supervised learning methods where a large amount of unlabeled data is utilized to improve the model's accuracy despite limited access to labeled examples. We propose a new framework dubbed SemiFed that unifies two dominant approaches for semi-supervised learning: consistency regularization and pseudo-labeling. SemiFed first applies advanced data augmentation techniques to enforce consistency regularization and then generates pseudo-labels using the model's predictions during training. SemiFed takes advantage of the federation so that for a given image, the pseudo-label holds only if multiple models from different clients produce a high-confidence prediction and agree on the same label. Extensive experiments on two image benchmarks demonstrate the effectiveness of our approach under both homogeneous and heterogeneous data distribution settings

本文提出了一种名为SemiFed的新框架，它将一致性正则化和伪标记方法融合到了联合学习中，以解决跨数据中心联合学习中的部分标记问题，并证明了该方法在同质化和异质化数据分布设置下的有效性。

SemiFed: 带一致性和伪标记的半监督联邦学习