  Federated learning is designed to enhance data security and privacy, but
faces challenges when dealing with heterogeneous data in long-tailed and
non-IID distributions. This paper explores an overlooked scenario where tail
classes are sparsely distributed over a few clients, causing the models trained
with these classes to have a lower probability of being selected during client
aggregation, leading to slower convergence rates and poorer model performance.
To address this issue, we propose a two-stage Decoupled Federated learning
framework using Feature Statistics (DFL-FS). In the first stage, the server
estimates the client's class coverage distributions through masked local
feature statistics clustering to select models for aggregation to accelerate
convergence and enhance feature learning without privacy leakage. In the second
stage, DFL-FS employs federated feature regeneration based on global feature
statistics and utilizes resampling and weighted covariance to calibrate the
global classifier to enhance the model's adaptability to long-tailed data
distributions. We conducted experiments on CIFAR10-LT and CIFAR100-LT datasets
with various long-tailed rates. The results demonstrate that our method
outperforms state-of-the-art methods in both accuracy and convergence rate.
