Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. Advanced FSSL methods predominantly focus on training a single model on each client. However, this approach could lead to a discrepancy between the objective functions of labeled and unlabeled data, resulting in gradient conflicts. To alleviate gradient conflict, we propose a novel twin-model paradigm, called Twin-sight, designed to enhance mutual guidance by providing insights from different perspectives of labeled and unlabeled data. In particular, Twin-sight concurrently trains a supervised model with a supervised objective function while training an unsupervised model using an unsupervised objective function. To enhance the synergy between these two models, Twin-sight introduces a neighbourhood-preserving constraint, which encourages the preservation of the neighbourhood relationship among data features extracted by both models. Our comprehensive experiments on four benchmark datasets provide substantial evidence that Twin-sight can significantly outperform state-of-the-art methods across various experimental settings, demonstrating the efficacy of the proposed Twin-sight.

翻译：联邦半监督学习（FSSL）已发展成为一种利用标签不足的分布式数据协作训练机器学习模型的有效范式。先进的FSSL方法主要侧重于在每个客户端上训练单个模型。然而，这种方法可能导致标注数据与未标注数据目标函数之间的差异，从而引发梯度冲突。为解决梯度冲突问题，我们提出一种新颖的双模型范式——Twin-sight，旨在通过从标注与未标注数据的不同视角提供洞察来增强相互指导。具体而言，Twin-sight同时训练一个采用有监督目标函数的监督模型和一个使用无监督目标函数的无监督模型。为增强这两个模型之间的协同作用，Twin-sight引入了一种邻域保持约束，该约束鼓励保留两个模型提取的数据特征间的邻域关系。在四个基准数据集上的全面实验提供了充分证据，表明Twin-sight在各种实验设置下能够显著超越现有最优方法，验证了所提Twin-sight的有效性。