  Federated learning (FL) provides a privacy-preserving solution for
distributed machine learning tasks. One challenging problem that severely
damages the performance of FL models is the co-occurrence of data heterogeneity
and long-tail distribution, which frequently appears in real FL applications.
In this paper, we reveal an intriguing fact that the biased classifier is the
primary factor leading to the poor performance of the global model. Motivated
by the above finding, we propose a novel and privacy-preserving FL method for
heterogeneous and long-tailed data via Classifier Re-training with Federated
Features (CReFF). The classifier re-trained on federated features can produce
comparable performance as the one re-trained on real data in a
privacy-preserving manner without information leakage of local data or class
distribution. Experiments on several benchmark datasets show that the proposed
CReFF is an effective solution to obtain a promising FL model under
heterogeneous and long-tailed data. Comparative results with the
state-of-the-art FL methods also validate the superiority of CReFF. Our code is
available at https://github.com/shangxinyi/CReFF-FL.
