| --- |
| |
| license: cc-by-nc-4.0 |
| tags: |
| - federated-learning |
| - foundation-models |
| - medical-imaging |
| - endoscopy |
| - self-supervised-learning |
| - masked-autoencoder |
| - contrastive-learning |
| - pytorch |
|
|
| --- |
| |
| # FedFound: Federated Foundation Models for Gastrointestinal Endoscopy |
|
|
| This repository contains pretrained foundation models released as part of the paper: |
|
|
| **FedFound: Federated Foundation Models for Gastrointestinal Endoscopy** |
|
|
| The models were trained using self-supervised learning on gastrointestinal endoscopy images under centralized, local, and federated learning settings. Two pretraining paradigms are provided: |
|
|
| * **Masked Autoencoder (MAE)** |
| * **Momentum Contrast (MoCo)** |
|
|
| These checkpoints can be used as initialization for downstream gastrointestinal endoscopy tasks such as classification, segmentation, and representation learning. |
|
|
| --- |
|
|
| ## Available Checkpoints |
|
|
| | Checkpoint | Clients | Pretraining | |
| | ---------------------- | ----------- | ----------- | |
| | lb_split1.pth | 1 | MAE | |
| | lb_split2.pth | 1 | MAE | |
| | lb_split10.pth | 10 | MAE | |
| | lb_split20.pth | 20 | MAE | |
| | ub_central.pth | Centralized | MAE | |
| | fedavg_split1.pth | 6 | MAE | |
| | fedavg_split2.pth | 6 | MAE | |
| | fedavg_split10.pth | 10 | MAE | |
| | fedavg_split20.pth | 20 | MAE | |
| | fedavgm_split1.pth | 6 | MAE | |
| | fedavgm_split2.pth | 6 | MAE | |
| | fedavgm_split10.pth | 10 | MAE | |
| | fedavgm_split20.pth | 20 | MAE | |
| | fedadam_split1.pth | 6 | MAE | |
| | fedadam_split2.pth | 6 | MAE | |
| | fedadam_split10.pth | 10 | MAE | |
| | fedadam_split20.pth | 20 | MAE | |
| | fedadagrad_split1.pth | 6 | MAE | |
| | fedadagrad_split2.pth | 6 | MAE | |
| | fedadagrad_split10.pth | 10 | MAE | |
| | fedadagrad_split20.pth | 20 | MAE | |
| | moco_lb_split1.pth | 1 | MoCo | |
| | moco_lb_split2.pth | 1 | MoCo | |
| | moco_ub_central.pth | Centralized | MoCo | |
| | moco_fedavg_split1.pth | 6 | MoCo | |
| | moco_fedavg_split2.pth | 6 | MoCo | |
| |
| --- |
| |
| ## Naming Convention |
| |
| * **lb**: Lower Bound (single-client training) |
| * **ub**: Upper Bound (centralized training) |
| * **fedavg**: FedAvg aggregation |
| * **fedavgm**: FedAvgM aggregation |
| * **fedadam**: FedAdam aggregation |
| * **fedadagrad**: FedAdagrad aggregation |
| * **moco**: Momentum Contrast (MoCo) pretraining |
| * Models without the `moco` prefix use Masked Autoencoder (MAE) pretraining |
| |
| --- |
| |
| ## Usage |
| |
| ```python |
| import torch |
| |
| checkpoint = torch.load("fedavg_split1.pth", map_location="cpu") |
| |
| if isinstance(checkpoint, dict) and "model" in checkpoint: |
| state_dict = checkpoint["model"] |
| else: |
| state_dict = checkpoint |
| |
| model.load_state_dict(state_dict, strict=False) |
| ``` |
| |
| |
| ## Repository Contents |
| |
| This repository contains only pretrained model weights. |
| |
| No patient images, labels, metadata, or clinical information are included. |
| |
| --- |
| |
| ## Citation |
| |
| If you use these models in your research, please cite: |
| |
| ```bibtex |
| @article{devkota2025federated, |
| title={Federated foundation model for gi endoscopy images}, |
| author={Devkota, Alina and Amireskandari, Annahita and Palko, Joel and Thakkar, Shyam and Adjeroh, Donald and Jiang, Xiajun and Bhattarai, Binod and Gyawali, Prashnna K}, |
| journal={arXiv preprint arXiv:2505.24108}, |
| year={2025} |
| } |
| ``` |
| |
| --- |
| |
| ## Contact |
| |
| For questions regarding the models, datasets, or training procedures, please open an issue or contact the authors of the paper. |
| |
