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Budget-Aware Synthetic Data Augmentation Improves Federated Learning Accuracy.

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

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Summary

This paper introduces FedEAS, a policy for synthetic data augmentation in federated learning that adaptively assigns per-client generation budgets based on local label distributions. FedEAS significantly reduces computational cost while recovering most accuracy gains compared to full class balancing, outperforming uniform allocation.

Federated learning often suffers from "client drift" and reduced global accuracy due to imbalanced data distributions across different clients, known as label skew. While generating synthetic data can help balance these distributions, fully balancing all classes can be computationally expensive. Researchers have developed FedEAS, a new strategy that intelligently allocates budgets for synthetic data generation. Instead of a fixed total budget, FedEAS calculates an entropy-adaptive budget for each client and each class, determining both the quantity and destination of generated samples. This approach allows the total generation budget to emerge dynamically. The method demonstrates substantial efficiency improvements, achieving most of the accuracy benefits of full class balancing with a 94.1% reduction in generation budget.

Why it matters

Professionals deploying federated learning systems can achieve higher model accuracy with significantly reduced computational overhead for data augmentation, making FL more practical and scalable.

How to implement this in your domain

  1. 1Evaluate existing federated learning pipelines for label skew and its impact on model performance.
  2. 2Investigate integrating adaptive synthetic data generation policies like FedEAS into current FL frameworks.
  3. 3Benchmark the computational cost and accuracy improvements of budget-aware augmentation against current data handling strategies.
  4. 4Develop strategies for monitoring client-side data distributions to inform dynamic budget allocation for synthetic data.

Who benefits

HealthcareFinanceIoTAutomotiveTelecommunications

Key takeaways

  • Label skew is a major challenge in federated learning, degrading global model accuracy.
  • Synthetic data augmentation can mitigate label skew but can be computationally intensive.
  • FedEAS introduces a budget-aware policy for synthetic data generation, optimizing resource use.
  • This approach significantly reduces generation costs while maintaining high accuracy in federated learning.

Original post by Sangwoo Lee, Sunghwan Park, Jaewoo Lee

"arXiv:2607.06616v1 Announce Type: new 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…"

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

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