# PathFL: Multi-Alignment Federated Learning for Pathology Image Segmentation

arXiv: https://arxiv.org/abs/2505.22522 · HTML: https://mlsanity.com/paper/2505.22522/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 47 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 23 of 23)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest](https://mlsanity.com/paper/1603.00275/) (2016)
- [MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images](https://mlsanity.com/paper/1806.01963/) (2018)
- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [Methods for Segmentation and Classification of Digital Microscopy Tissue Images](https://mlsanity.com/paper/1810.13230/) (2018)
- [On Feature Normalization and Data Augmentation](https://mlsanity.com/paper/2002.11102/) (2020)
- [HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images](https://mlsanity.com/paper/2112.10775/) (2021)
- [Federated Learning for Computational Pathology on Gigapixel Whole Slide Images](https://mlsanity.com/paper/2009.10190/) (2020)
- [CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images](https://mlsanity.com/paper/2101.00442/) (2021)
- [ExFuse: Enhancing Feature Fusion for Semantic Segmentation](https://mlsanity.com/paper/1804.03821/) (2018)
- [Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity](https://mlsanity.com/paper/2405.16585/) (2024)
- [Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts](https://mlsanity.com/paper/2312.02567/) (2023)
- [Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation](https://mlsanity.com/paper/2102.01678/) (2021)
- [Federated Stain Normalization for Computational Pathology](https://mlsanity.com/paper/2209.14849/) (2022)
- [FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification](https://mlsanity.com/paper/2302.12662/) (2023)
- [FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation](https://mlsanity.com/paper/2411.04509/) (2024)

## Built on by (most cited first, 3 of 3)

- [FedStain: Modeling Higher-Order Stain Statistics for Federated Domain Generalization in Computational Pathology](https://mlsanity.com/paper/2605.14590/) (2026)
- [FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentation](https://mlsanity.com/paper/2603.11659/) (2026)
- [Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation](https://mlsanity.com/paper/2610.04700/) (2026)

## Abstract (arXiv)

Pathology image segmentation across multiple centers encounters significant challenges due to diverse sources of heterogeneity including imaging modalities, organs, and scanning equipment, whose variability brings representation bias and impedes the development of generalizable segmentation models. In this paper, we propose PathFL, a novel multi-alignment Federated Learning framework for pathology image segmentation that addresses these challenges through three-level alignment strategies of image, feature, and model aggregation. Firstly, at the image level, a collaborative style enhancement module aligns and diversifies local data by facilitating style information exchange across clients. Secondly, at the feature level, an adaptive feature alignment module ensures implicit alignment in the representation space by infusing local features with global insights, promoting consistency across heterogeneous client features learning. Finally, at the model aggregation level, a stratified similarity aggregation strategy hierarchically aligns and aggregates models on the server, using layer-specific similarity to account for client discrepancies and enhance global generalization. Comprehensive evaluations on four sets of heterogeneous pathology image datasets, encompassing cross-source, cross-modality, cross-organ, and cross-scanner variations, validate the effectiveness of our PathFL in achieving better performance and robustness against data heterogeneity.