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

arXiv cs.LG ↗ · 2026-07-09 Cached

Proposes FedEAS, a budget-aware policy for synthetic data augmentation in federated learning that assigns each client an entropy-adaptive per-class generation budget, recovering most accuracy gains of full class balancing while reducing generation cost by 94.1%.

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