Federated Semi-supervised Learning (FSSL) combines techniques from both fields of federated and semi-supervised learning to improve the accuracy and performance of models in a distributed environment by using a small fraction of labeled data and a large amount of unlabeled data. Without the need to centralize all data in one place for training, it collect updates of model training after devices train models at local, and thus can protect the privacy of user data. However, during the federal training process, some of the devices fail to collect enough data for local training, while new devices will be included to the group training. This leads to an unbalanced global data distribution and thus affect the performance of the global model training. Most of the current research is focusing on class imbalance with a fixed number of classes, while little attention is paid to data imbalance with a variable number of classes. Therefore, in this paper, we propose Federated Semi-supervised Learning for Class Variable Imbalance (FCVI) to solve class variable imbalance. The class-variable learning algorithm is used to mitigate the data imbalance due to changes of the number of classes. Our scheme is proved to be significantly better than baseline methods, while maintaining client privacy.

翻译：联邦半监督学习（FSSL）结合了联邦学习与半监督学习领域的技术，通过利用少量标注数据和大量未标注数据，在分布式环境中提升模型的精度与性能。该方法无需将所有数据集中到一处进行训练，而是通过设备在本地完成模型训练后聚合模型更新，从而保护用户数据隐私。然而，在联邦训练过程中，部分设备未能收集足够数据进行本地训练，同时新设备可能被纳入组训练，导致全局数据分布不平衡，进而影响全局模型训练性能。当前大多数研究聚焦于固定类别数量的类别不平衡问题，而对类别数量可变的数据不平衡问题关注甚少。为此，本文提出面向类别可变不平衡的联邦半监督学习（FCVI）方法，利用类别可变学习算法缓解因类别数量变化导致的数据不平衡。实验证明，该方案在保护客户端隐私的前提下，显著优于基线方法。