WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning

arXiv:2607.06616v1 Announce Type: new Abstract: Label skew in federated learning (FL) causes client drift and degrades global accuracy. Synthetic data augmentation can reduce this imbalance; however, full class balancing requires substantial computation cost. We propose FedEAS, a policy that assigns each client an entropy-adaptive per-class generation budget computed from its local label distribution. The budget jointly decides \emph{how much} each client generates and \emph{WHERE} the samples g...