This repository contains the implementation for our study on hybrid data selection in federated learning. We propose a warm-up based method that scores local samples using entropy, EL2N, gradient norms, and stability, and selects a high-quality subset for training. The repo includes full FedAvg baselines, random baselines, hybrid selection, ablations, keep-ratio experiments, and robustness tests across different Dirichlet non-IID settings.