Decoupled Federated Learning Framework for Long-Tailed and Non-IID Data with Feature Statistics

The author proposes a Decoupled Federated Learning framework using Feature Statistics to address challenges in long-tailed and non-IID data scenarios, focusing on model convergence and performance enhancement. The main thesis of the author is to introduce a two-stage approach that leverages feature statistics for client selection and classifier retraining to improve model adaptability and performance in federated learning settings.