[Submitted on 7 Jul 2026]
Title:WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning
View a PDF of the paper titled WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning, by Sangwoo Lee and 2 other authors
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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
Submission history
From: Sunghwan Park [view email] [v1] Tue, 7 Jul 2026 08:25:04 UTC (1,090 KB)
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