No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data Mi Luo

A central challenge in training classification models in the real-world federated system is learning with non-IID data. To cope with this, most of the existing works involve enforcing regularization in local optimization or improving the model aggregation scheme at the server.