# Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer

arXiv: https://arxiv.org/abs/2210.00912 · HTML: https://mlsanity.com/paper/2210.00912/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 43 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [Self-Challenging Improves Cross-Domain Generalization](https://mlsanity.com/paper/2007.02454/) (2020)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [A Fourier-based Framework for Domain Generalization](https://mlsanity.com/paper/2105.11120/) (2021)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [Generalizing Across Domains via Cross-Gradient Training](https://mlsanity.com/paper/1804.10745/) (2018)
- [Episodic Training for Domain Generalization](https://mlsanity.com/paper/1902.00113/) (2019)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](https://mlsanity.com/paper/1603.03417/) (2016)
- [Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization](https://mlsanity.com/paper/2110.10832/) (2021)
- [Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis](https://mlsanity.com/paper/2101.04775/) (2021)
- [Efficient Domain Generalization via Common-Specific Low-Rank Decomposition](https://mlsanity.com/paper/2003.12815/) (2020)
- [Learning to Learn with Variational Information Bottleneck for Domain Generalization](https://mlsanity.com/paper/2007.07645/) (2020)
- [Fast Patch-based Style Transfer of Arbitrary Style](https://mlsanity.com/paper/1612.04337/) (2016)

## Built on by (most cited first, 30 of 40)

- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [Robust Visual Question Answering: Datasets, Methods, and Future Challenges](https://mlsanity.com/paper/2307.11471/) (2023)
- [StableFDG: Style and Attention Based Learning for Federated Domain Generalization](https://mlsanity.com/paper/2311.00227/) (2023)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization](https://mlsanity.com/paper/2401.10272/) (2024)
- [Robust Split Federated Learning for U-shaped Medical Image Networks](https://mlsanity.com/paper/2212.06378/) (2022)
- [Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization](https://mlsanity.com/paper/2403.15605/) (2024)
- [Federated Unsupervised Domain Generalization using Global and Local Alignment of Gradients](https://mlsanity.com/paper/2405.16304/) (2024)
- [Federated Domain Generalization with Data-free On-server Matching Gradient](https://mlsanity.com/paper/2501.14653/) (2025)
- [FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment](https://mlsanity.com/paper/2501.15486/) (2025)
- [Feature Diversification and Adaptation for Federated Domain Generalization](https://mlsanity.com/paper/2407.08245/) (2024)
- [Federated Out-of-Distribution Generalization: A Causal Augmentation View](https://mlsanity.com/paper/2504.19882/) (2025)
- [Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization](https://mlsanity.com/paper/2505.04979/) (2025)
- [Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery](https://mlsanity.com/paper/2408.03208/) (2024)
- [Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures](https://mlsanity.com/paper/2409.13527/) (2024)
- [Global Intervention and Distillation for Federated Out-of-Distribution Generalization](https://mlsanity.com/paper/2504.00850/) (2025)
- [FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning](https://mlsanity.com/paper/2410.11267/) (2024)
- [Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training](https://mlsanity.com/paper/2412.11408/) (2024)
- [FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2501.07378/) (2025)
- [FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis](https://mlsanity.com/paper/2501.13967/) (2025)
- [FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification](https://mlsanity.com/paper/2511.12044/) (2025)
- [Reducing Spurious Correlation for Federated Domain Generalization](https://mlsanity.com/paper/2407.19174/) (2024)
- [Generalized Task-Driven Medical Image Quality Enhancement with Gradient Promotion](https://mlsanity.com/paper/2501.01114/) (2025)
- [FedS2R: One-Shot Federated Domain Generalization for Synthetic-to-Real Semantic Segmentation in Autonomous Driving](https://mlsanity.com/paper/2507.19881/) (2025)
- [FedStain: Modeling Higher-Order Stain Statistics for Federated Domain Generalization in Computational Pathology](https://mlsanity.com/paper/2605.14590/) (2026)
- [FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection](https://mlsanity.com/paper/2308.10236/) (2023)
- [Federated Learning with New Knowledge: Fundamentals, Advances, and Futures](https://mlsanity.com/paper/2402.02268/) (2024)
- [FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning](https://mlsanity.com/paper/2410.13045/) (2024)

## Abstract (arXiv)

Domain generalization (DG) has been a hot topic in image recognition, with a goal to train a general model that can perform well on unseen domains. Recently, federated learning (FL), an emerging machine learning paradigm to train a global model from multiple decentralized clients without compromising data privacy, brings new challenges, also new possibilities, to DG. In the FL scenario, many existing state-of-the-art (SOTA) DG methods become ineffective, because they require the centralization of data from different domains during training. In this paper, we propose a novel domain generalization method for image recognition under federated learning through cross-client style transfer (CCST) without exchanging data samples. Our CCST method can lead to more uniform distributions of source clients, and thus make each local model learn to fit the image styles of all the clients to avoid the different model biases. Two types of style (single image style and overall domain style) with corresponding mechanisms are proposed to be chosen according to different scenarios. Our style representation is exceptionally lightweight and can hardly be used for the reconstruction of the dataset. The level of diversity is also flexible to be controlled with a hyper-parameter. Our method outperforms recent SOTA DG methods on two DG benchmarks (PACS, OfficeHome) and a large-scale medical image dataset (Camelyon17) in the FL setting. Last but not least, our method is orthogonal to many classic DG methods, achieving additive performance by combined utilization.