WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning
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Computer Science > Machine Learning
Title:WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning
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 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.
| Comments: | preprint |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.06616 [cs.LG] |
| (or arXiv:2607.06616v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.06616
arXiv-issued DOI via DataCite
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