# Anatomical 3D Style Transfer Enabling Efficient Federated Learning with Extremely Low Communication Costs

arXiv: https://arxiv.org/abs/2410.20102 · HTML: https://mlsanity.com/paper/2410.20102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023)
- [The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge](https://mlsanity.com/paper/1912.01054/) (2019)
- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)
- [DENSE: Data-Free One-Shot Federated Learning](https://mlsanity.com/paper/2112.12371/) (2021)
- [Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation](https://mlsanity.com/paper/2203.10144/) (2022)
- [Learning Federated Visual Prompt in Null Space for MRI Reconstruction](https://mlsanity.com/paper/2303.16181/) (2023)
- [Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning](https://mlsanity.com/paper/2210.02326/) (2022)
- [Federated Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2204.10983/) (2022)
- [DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks](https://mlsanity.com/paper/2012.11025/) (2020)
- [Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes](https://mlsanity.com/paper/2403.16499/) (2024)
- [Federated Multi-organ Segmentation with Inconsistent Labels](https://mlsanity.com/paper/2206.07156/) (2022)
- [Transfer learning for multi-center classification of chronic obstructive pulmonary disease](https://mlsanity.com/paper/1701.05013/) (2017)
- [ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals](https://mlsanity.com/paper/2212.05602/) (2022)

## Abstract (arXiv)

In this study, we propose a novel federated learning (FL) approach that utilizes 3D style transfer for the multi-organ segmentation task. The multi-organ dataset, obtained by integrating multiple datasets, has high scalability and can improve generalization performance as the data volume increases. However, the heterogeneity of data owing to different clients with diverse imaging conditions and target organs can lead to severe overfitting of local models. To align models that overfit to different local datasets, existing methods require frequent communication with the central server, resulting in higher communication costs and risk of privacy leakage. To achieve an efficient and safe FL, we propose an Anatomical 3D Frequency Domain Generalization (A3DFDG) method for FL. A3DFDG utilizes structural information of human organs and clusters the 3D styles based on the location of organs. By mixing styles based on these clusters, it preserves the anatomical information and leads models to learn intra-organ diversity, while aligning the optimization of each local model. Experiments indicate that our method can maintain its accuracy even in cases where the communication cost is highly limited (=1.25% of the original cost) while achieving a significant difference compared to baselines, with a higher global dice similarity coefficient score of 4.3%. Despite its simplicity and minimal computational overhead, these results demonstrate that our method has high practicality in real-world scenarios where low communication costs and a simple pipeline are required. The code used in this project will be publicly available.