$F^{2}DC$ - Federated Feature Decoupling and Calibration (CVPR 2026)

Official implementation of CVPR 2026 paper: 'Domain-Skewed Federated Learning with Feature Decoupling and Calibration'.

Abstract

Federated Learning (FL) allows distributed clients to collaboratively train a global model in a privacy-preserving manner. However, one major challenge is domain skew, where clients' data originating from diverse domains may hinder the aggregated global model from learning a consistent representation space, resulting in poor generalizable ability in multiple domains. In this paper, we argue that the domain skew is reflected in the domain-specific biased features of each client, causing the local model's representations to collapse into a narrow low-dimensional subspace. We then propose Federated Feature Decoupling and Calibration ($F^{2}DC$), which liberates valuable class-relevant information by calibrating the domain-specific biased features, enabling more consistent representations across domains. A novel component, Domain Feature Decoupler (DFD), is first introduced in $F^{2}DC$ to determine the robustness of each feature unit, thereby separating the local features into domain-robust features and domain-related features. A Domain Feature Corrector (DFC) is further proposed to calibrate these domain-related features by explicitly linking discriminative signals, capturing additional class-relevant clues that complement the domain-robust features. Finally, a domain-aware aggregation of the local models is performed to promote consensus among clients. Empirical results on three popular multi-domain datasets demonstrate the effectiveness of the proposed $F^{2}DC$ and the contributions of its two modules.

Setup Libraries

• python >= 3.10.11
• torch >= 1.13.0
• torchvision >= 0.14.0
• scipy >= 1.10.1
• scikit-image >= 0.21.0
• numpy >= 1.24.3
• tqdm >= 4.64.0

Multi-domain Datasets

• Digits: include 4 domains (MNIST, USPS, SVHN, SYN). 【Download Link -> [Google Drive]】
• Office-Caltech: include 4 domains (Caltech, Amazon, Webcam, DSLR). 【Download Link -> [Google Drive]】
• PACS: include 4 domains (Photo, Art-Painting, Cartoon, Sketch). 【Download Link -> [Google Drive]】
• After downloading these datasets, please place them in the "./rundata/dataset/" folder.

Run Experiments

• Run $F^{2}DC$ on Digits:

  python3 main_run.py --parti_num 20 --model f2dc --dataset fl_digits

• Run $F^{2}DC$ on Office-Caltech:

  python3 main_run.py --parti_num 10 --model f2dc --dataset fl_officecaltech

• Run $F^{2}DC$ on PACS:

  python3 main_run.py --parti_num 10 --model f2dc --dataset fl_pacs

Citation

@inproceedings{WangF2DC_CVPR26,
    author={Wang, Huan and Shen, Jun and Yan, Jun and Pang, Guansong},
    title={Domain-Skewed Federated Learning with Feature Decoupling and Calibration},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    year={2026}
}