The source code of our works on federated learning:
- CVPR 2022 paper: Federated Learning with Position-Aware Neurons.
- Personal Homepage
- Basic Introduction
- Running Tips
- Citation
- More 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 (downloading link code be found in my Homepage):
- CIFAR-10
- CIFAR-100
python train_fedpan.py
: notice that FedPAN differs from FedAvg only in the utilized networks, where the former uses networks with PANs (Position-Aware Neurons).- The hyper-parameters of PANs: pe_way (default is "sin"), pe_t (default is 1.0, T in the paper), pe_op ("add" or "mul", two types of PANs in the paper), pe_alpha (A in the paper), pe_ratio (a constant of 1.0). pe_op and pe_alpha matters in FedPAN.
FL algorithms and hyper-parameters could be set in these files.
- Xin-Chun Li, Yi-Chu Xu, Shaoming Song, Bingshuai Li, Yinchuan Li, Yunfeng Shao, De-Chuan Zhan. Federated Learning with Position-Aware Neurons. In: Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'2022), online conference, New Orleans, Louisiana, 2022.
- [BibTex]