FedConv

• Pytorch implementation for paper: FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning
• Note that we simulate Federated Learning in one local machine for research usage, and do not involve real communication between different clients.

Usage

0. Installation

• Run cd FedConv
• Install the libraries listed in requirements.txt

1. Prepare Dataset

We provide the data partitions for CIFAR-10, COVID-FL, and iNaturalist datasets.

• CIFAR-10 dataset

  • Download the three sets of simulated data partitions from CIFAR-10
  • Put the downloaded cifar10.npy at sub-folder data

• COVID-FL dataset

  • Download the data and partitions file from COVID-FL

• iNaturalist dataset

  • Download the partition following instructions from FedScale

2. Set (download) the Pretrained Models

• We provide our models pretrained from Imagenet-1k
  • FedConv-Normal
  • FedConv-Invert
  • FedConv-InvertUp
• Then put the pretrained model under the sub-folder checkpoint

3. Train Model

• Use the commands below to train models in different datasets

  • CIFAR-10: bash cifar_fedconv.sh
  • COVID-FL: bash covid_fedconv.sh
  • iNatualist: bash inat_fedconv.sh

• All the checkpoints, results, and log files will be saved to the --output_dir folder, with the final performance saved at log_file.txt

4. Trained Models Checkpoint

• We provide our models trained and validated in the COVID-FL dataset
  • FedConv-Normal
  • FedConv-Invert
  • FedConv-InvertUp

Additional Notes

• Some important tags for training setting:

  • --net_name: name of models to run. In our works, you can choose models directly from resnet50, vit_small_patch16_224, swin_tiny_patch4_window7_224, convnext_tiny, fedconv_base, fedconv_invert, and fedconv_invertup.
  • --dataset: we provide implement of CIFAR-10 and COVID-FL in main.py, iNatualist in main_select.py
  • --save_model_flag: set to True if need to save the checkpoints
  • --output_dir: the output directory where checkpoints/results/logs will be written
  • --E_epoch: local training epoch E in FL train
  • --max_communication_rounds: total communication rounds, set 100 in default.
  • --split_type: type of data partitions, supports ["split_1", "split_2", "split_3"] for CIFAR-10, ["real_test"] for COVID-FL and iNatualist.
  • --num_local_clients: Num of local clients joined in each FL train. -1 (usage of all local clients) for CIFAR-10 and COVID-FL, 25 for iNaturalist.

• Also refer to the main.py and main_select.py for more tags

Acknowledgments

• This work is supported by a gift from Open Philanthropy, TPU Research Cloud Program, and Google Cloud Research Credits program.
• ResNet50, ViT, Swin-Transformer, and ConvNext implementations are based on https://github.com/rwightman/pytorch-image-models
• Our code is based on https://github.com/Liangqiong/ViT-FL-main

Citation

@article{xu2024fedconv,
   title   = {FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning},
   author  = {Xu, Peiran and Wang, Zeyu and Mei, Jieru and Qu, Liangqiong and Yuille, Alan and Xie, Cihang and Zhou, Yuyin},
   journal = {TMLR},
   year    = {2024}
}