Federated Learning (FL) enables the multiple participating devices to collaboratively contribute a global neural network model while keeping training data locally. Unlike centralized setting, non-IID, imbalanced (statistical heterogeneity) and distribution shifted of FL is distributed in federated network, which will increase divergences between local models model, further degrading performance...
