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FedFound

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federated-learning
foundation-models
medical-imaging
endoscopy
self-supervised-learning
masked-autoencoder
contrastive-learning
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  1. README.md +118 -0
  2. fedadagrad_split1.pth +3 -0
  3. fedadagrad_split10.pth +3 -0
  4. fedadagrad_split2.pth +3 -0
  5. fedadagrad_split20.pth +3 -0
  6. fedadam_split1.pth +3 -0
  7. fedadam_split10.pth +3 -0
  8. fedadam_split2.pth +3 -0
  9. fedadam_split20.pth +3 -0
  10. fedavg_split1.pth +3 -0
  11. fedavg_split10.pth +3 -0
  12. fedavg_split2.pth +3 -0
  13. fedavg_split20.pth +3 -0
  14. fedavgm_split1.pth +3 -0
  15. fedavgm_split10.pth +3 -0
  16. fedavgm_split2.pth +3 -0
  17. fedavgm_split20.pth +3 -0
  18. lb_split1.pth +3 -0
  19. lb_split10.pth +3 -0
  20. lb_split2.pth +3 -0
  21. lb_split20.pth +3 -0
  22. moco_fedavg_split1.pth +3 -0
  23. moco_fedavg_split2.pth +3 -0
  24. moco_lb_split1.pth +3 -0
  25. moco_lb_split2.pth +3 -0
  26. moco_ub_central.pth +3 -0
  27. ub_central.pth +3 -0
README.md ADDED
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+ ---
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+
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+ license: cc-by-nc-4.0
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+ tags:
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+ - federated-learning
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+ - foundation-models
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+ - medical-imaging
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+ - endoscopy
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+ - self-supervised-learning
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+ - masked-autoencoder
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+ - contrastive-learning
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+ - pytorch
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+
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+ ---
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+
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+ # FedFound: Federated Foundation Models for Gastrointestinal Endoscopy
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+
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+ This repository contains pretrained foundation models released as part of the paper:
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+
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+ **FedFound: Federated Foundation Models for Gastrointestinal Endoscopy**
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+
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+ The models were trained using self-supervised learning on gastrointestinal endoscopy images under centralized, local, and federated learning settings. Two pretraining paradigms are provided:
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+
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+ * **Masked Autoencoder (MAE)**
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+ * **Momentum Contrast (MoCo)**
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+
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+ These checkpoints can be used as initialization for downstream gastrointestinal endoscopy tasks such as classification, segmentation, and representation learning.
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+
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+ ---
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+
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+ ## Available Checkpoints
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+
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+ | Checkpoint | Clients | Pretraining |
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+ | ---------------------- | ----------- | ----------- |
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+ | lb_split1.pth | 1 | MAE |
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+ | lb_split2.pth | 1 | MAE |
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+ | lb_split10.pth | 10 | MAE |
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+ | lb_split20.pth | 20 | MAE |
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+ | ub_central.pth | Centralized | MAE |
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+ | fedavg_split1.pth | 6 | MAE |
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+ | fedavg_split2.pth | 6 | MAE |
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+ | fedavg_split10.pth | 10 | MAE |
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+ | fedavg_split20.pth | 20 | MAE |
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+ | fedavgm_split1.pth | 6 | MAE |
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+ | fedavgm_split2.pth | 6 | MAE |
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+ | fedavgm_split10.pth | 10 | MAE |
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+ | fedavgm_split20.pth | 20 | MAE |
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+ | fedadam_split1.pth | 6 | MAE |
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+ | fedadam_split2.pth | 6 | MAE |
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+ | fedadam_split10.pth | 10 | MAE |
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+ | fedadam_split20.pth | 20 | MAE |
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+ | fedadagrad_split1.pth | 6 | MAE |
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+ | fedadagrad_split2.pth | 6 | MAE |
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+ | fedadagrad_split10.pth | 10 | MAE |
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+ | fedadagrad_split20.pth | 20 | MAE |
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+ | moco_lb_split1.pth | 1 | MoCo |
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+ | moco_lb_split2.pth | 1 | MoCo |
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+ | moco_ub_central.pth | Centralized | MoCo |
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+ | moco_fedavg_split1.pth | 6 | MoCo |
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+ | moco_fedavg_split2.pth | 6 | MoCo |
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+
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+ ---
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+
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+ ## Naming Convention
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+
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+ * **lb**: Lower Bound (single-client training)
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+ * **ub**: Upper Bound (centralized training)
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+ * **fedavg**: FedAvg aggregation
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+ * **fedavgm**: FedAvgM aggregation
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+ * **fedadam**: FedAdam aggregation
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+ * **fedadagrad**: FedAdagrad aggregation
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+ * **moco**: Momentum Contrast (MoCo) pretraining
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+ * Models without the `moco` prefix use Masked Autoencoder (MAE) pretraining
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+
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+ ---
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+
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+ ## Usage
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+
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+ ```python
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+ import torch
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+
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+ checkpoint = torch.load("fedavg_split1.pth", map_location="cpu")
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+
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+ if isinstance(checkpoint, dict) and "model" in checkpoint:
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+ state_dict = checkpoint["model"]
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+ else:
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+ state_dict = checkpoint
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+
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+ model.load_state_dict(state_dict, strict=False)
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+ ```
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+
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+
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+ ## Repository Contents
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+
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+ This repository contains only pretrained model weights.
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+
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+ No patient images, labels, metadata, or clinical information are included.
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+
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+ ---
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+
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+ ## Citation
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+
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+ If you use these models in your research, please cite:
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+
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+ ```bibtex
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+ @article{devkota2025federated,
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+ title={Federated foundation model for gi endoscopy images},
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+ 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},
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+ journal={arXiv preprint arXiv:2505.24108},
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+ year={2025}
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+ }
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+ ```
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+
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+ ---
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+
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+ ## Contact
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+
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+ For questions regarding the models, datasets, or training procedures, please open an issue or contact the authors of the paper.
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