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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 go. Accordingly, the total generation budget follows from the per-client budgets rather than being fixed in advance. FedEAS recovers most of the accuracy gain of full class balancing while reducing the generation budget by 94.1\\%. At the same total generation budget, it outperforms Uniform allocation by up to 18.82\\% across CIFAR-10 and CIFAR-100.","title":"WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning","url":"https://arxiv.org/abs/2607.06616","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.06616v1 Announce Type: cross \nAbstract: 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 go. Accordingly, the total generation budget follows from the per-client budgets rather than being fixed in advance. FedEAS recovers most of the accuracy gain of full class balancing while reducing the generation budget by 94.1\\%. 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