# Frequency-Based Federated Domain Generalization for Polyp Segmentation

arXiv: https://arxiv.org/abs/2410.02044 · HTML: https://mlsanity.com/paper/2410.02044/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2024.

## Built on by (most cited first, 7 of 7)

- [Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI](https://mlsanity.com/paper/2410.22530/) (2024)
- [IPMN Risk Assessment under Federated Learning Paradigm](https://mlsanity.com/paper/2411.05697/) (2024)
- [Cyst-X: A Multi-Center MRI Benchmark and Federated Learning Framework for Malignancy-Risk Stratification of Pancreatic Cystic Neoplasm](https://mlsanity.com/paper/2507.22017/) (2025)
- [Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation](https://mlsanity.com/paper/2506.23334/) (2025)
- [TreeFedDG: Alleviating Global Drift in Federated Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2510.18268/) (2025)
- [BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization](https://mlsanity.com/paper/2608.10271/) (2026)
- [MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities](https://mlsanity.com/paper/2608.19788/) (2026)

## Abstract (arXiv)

Federated Learning (FL) offers a powerful strategy for training machine learning models across decentralized datasets while maintaining data privacy, yet domain shifts among clients can degrade performance, particularly in medical imaging tasks like polyp segmentation. This paper introduces a novel Frequency-Based Domain Generalization (FDG) framework, utilizing soft-thresholding and hard-thresholding in the Fourier domain to address these challenges. By applying soft-thresholding and hard-thresholding to Fourier coefficients, our method generates new images with reduced background noise and enhances the model's ability to generalize across diverse medical imaging domains. Extensive experiments demonstrate substantial improvements in segmentation accuracy and domain robustness over baseline methods. This innovation integrates frequency domain techniques into FL, presenting a resilient approach to overcoming domain variability in decentralized medical image analysis.