  The label distribution skew induced data heterogeniety has been shown to be a
significant obstacle that limits the model performance in federated learning,
which is particularly developed for collaborative model training over
decentralized data sources while preserving user privacy. This challenge could
be more serious when the participating clients are in unstable circumstances
and dropout frequently. Previous work and our empirical observations
demonstrate that the classifier head for classification task is more sensitive
to label skew and the unstable performance of FedAvg mainly lies in the
imbalanced training samples across different classes. The biased classifier
head will also impact the learning of feature representations. Therefore,
maintaining a balanced classifier head is of significant importance for
building a better global model. To this end, we propose a simple yet effective
framework by introducing a prior-calibrated softmax function for computing the
cross-entropy loss and a prototype-based feature augmentation scheme to
re-balance the local training, which are lightweight for edge devices and can
facilitate the global model aggregation. The improved model performance over
existing baselines in the presence of non-IID data and client dropout is
demonstrated by conducting extensive experiments on benchmark classification
tasks.
