Official implementation of the paper FedPD: Federated Open Set Recognition with Parameter Disentanglement (ICCV 2023).
cd data
pip install -e .
Benchmark(Digits)
- Please download our pre-processed datasets here, put under
data/directory and perform following commands:cd ./data unzip digit_dataset.zip
Baseline
Our local training is based on popolar open-set recognition framework Proser. A simple baseline for federated open-set recognition is provided:
python tools/proser_federated.py --log --mode fedavg
FedPD
To achieve parameter alignment for FedOSR, you can try:
python tools/proser_fedpd.py --log --mode fedpd
We sincerely thank Proser, DenseHybrid, and FedBN for providing their wonderful code!
Please use the following bib entry to cite the paper if you are using resources from this repo.
@InProceedings{Yang_2023_ICCV,
author = {Yang, Chen and Zhu, Meilu and Liu, Yifan and Yuan, Yixuan},
title = {FedPD: Federated Open Set Recognition with Parameter Disentanglement},
booktitle = {IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2023},
}