  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.
