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FedEAS Optimizes Synthetic Data Generation for Federated Learning

Sangwoo Lee, Sunghwan Park, Jaewoo Lee· July 9, 2026 View original

Summary

This paper introduces FedEAS, a policy for budget-aware synthetic data augmentation in label-skewed federated learning. FedEAS adaptively assigns per-class generation budgets to clients based on local label distributions, significantly reducing computational cost while recovering most accuracy gains of full class balancing.

Federated learning (FL) often suffers from performance degradation due to label skew, where data labels are unevenly distributed across client devices. While synthetic data augmentation can help balance these distributions, fully balancing all classes is computationally expensive. This research proposes FedEAS, a novel policy designed for budget-aware synthetic augmentation that intelligently allocates generation resources. FedEAS assigns each client an entropy-adaptive, per-class generation budget, which is calculated based on its local label distribution. This budget determines both the quantity of synthetic data each client generates and the specific classes for which it generates samples. This dynamic allocation strategy allows the total generation budget to emerge from client-specific needs rather than being fixed upfront. Experiments on CIFAR-10 and CIFAR-100 datasets demonstrate that FedEAS recovers most of the accuracy benefits of full class balancing while reducing the overall generation budget by an impressive 94.1%. It also outperforms uniform allocation strategies by up to 18.82% for the same total budget.

Why it matters

This innovation makes federated learning more efficient and effective in real-world scenarios where data imbalance is common, enabling better model performance with significantly reduced computational overhead.

How to implement this in your domain

  1. 1Integrate FedEAS or similar budget-aware augmentation policies into your federated learning pipelines.
  2. 2Prioritize synthetic data generation for underrepresented classes based on client-specific label distributions.
  3. 3Evaluate the trade-off between computational budget and model accuracy when designing data augmentation strategies.
  4. 4Explore the application of entropy-adaptive budgeting to other resource-constrained distributed learning tasks.

Who benefits

HealthcareMobile ComputingIoTSmart CitiesAutomotive

Key takeaways

  • FedEAS efficiently addresses label skew in federated learning through budget-aware synthetic augmentation.
  • It adaptively assigns per-class generation budgets based on local label distributions.
  • The policy significantly reduces computational costs while maintaining high accuracy.
  • FedEAS outperforms uniform allocation strategies for synthetic data generation.

Original post by Sangwoo Lee, Sunghwan Park, Jaewoo Lee

"arXiv:2607.06616v1 Announce Type: cross 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,…"

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Originally posted by Sangwoo Lee, Sunghwan Park, Jaewoo Lee on X · view source

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