The source code of our works on federated learning:
- Submitted to ECML-PKDD 2023 Journal Track (Data Mining and Knowledge Discovery, DMKD Journal): MrTF: Model Refinery for Transductive Federated Learning.
- Personal Homepage
- Basic Introduction
- Running Tips
- Citation
- We consider a real-world scenario that a newly-established pilot project needs to make inferences for newly-collected data, but it does not have any labeled data for training.
- We resort to federated learning (FL) and abstract this scene as transductive federated learning (TFL).
- To facilitate TFL, we propose several techniques including stabilized teachers, rectified distillation, and clustered label refinery.
- The proposed Model refinery framework for Transductive Federated learning (MrTF) shows superiorities towards other FL methods on several benchmarks.
- Related Federated Learning codes could be found in our FL repository FedRepo
The code files are written in Python, and the utilized deep learning tool is PyTorch.
python
: 3.7.3numpy
: 1.21.5torch
: 1.9.0torchvision
: 0.10.0pillow
: 8.3.1
We provide several datasets including (if can not download, please copy the links to a new browser window):
python train_fedavg.py
: the baseline of FedAvgpython train_feddf.py
: the baseline of FedDFpython train_mrtf.py
: our proposed algorithm for transductive federated learning.
FL algorithms and hyper-parameters could be set in these files.
- Xin-Chun Li, Yang Yang, De-Chuan Zhan. MrTF: Model Refinery for Transductive Federated Learning.
- [BibTex]