bafatah / ViT-FL-main

Vision Transformer in Federated Learning

• Pytorch implementation for paper: "Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning"
• Note that we simulate either CWT or FedAVG in one local machine for research usage, donot involve real communication between different clients.

Usage

0. Installation

• Run cd ViT-FL-main
• Install the libraries listed in requirements.txt

1. Prepare Dataset

We provide the data partitions for Cifar-10 and CelebA datasets

• Cifar-10 dataset

  • Download the three sets of simulated data partitions from https://drive.google.com/drive/folders/1ZErR7RMSVImkzYzz0hLl25f9agJwp0Zx?usp=sharing
  • Put the downloaded cifar10.npy at sub-folder data

• For CelebA dataset (refer to https://leaf.cmu.edu/ for more usage of CelebA dataset)

  • Get the raw images at https://drive.google.com/file/d/0B7EVK8r0v71pZjFTYXZWM3FlRnM/view?usp=sharing&resourcekey=0-dYn9z10tMJOBAkviAcfdyQ
  • Put the extracted raw image folder at sub-folder data
  • The pre-processed data distributions is provided CelebA.npy at sub-folder data

• Retina dataset (Coming soon)

2. Set (download) the Pretrained Models

• We use ImageNet1k pre-train in our paper
• For ViTs: To use ImageNet1K pretrained models for ViTs, please modify the loading link of pretrained models in timm mannually (modify the link setting of default_cfgs = { } in the timm/models/vision_transformer.py file):
  • For ViT(T),
    'vit_tiny_patch16_224': _cfg( url='https://storage.googleapis.com/vit_models/augreg/' 'Ti_16-i1k-300ep-lr_0.001-aug_light0-wd_0.1-do_0.0-sd_0.0.npz'),
  • For ViT(S), 'vit_small_patch16_224': _cfg( url='https://storage.googleapis.com/vit_models/augreg/' 'S_16-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0.npz'),
  • For ViT(B), 'vit_base_patch16_224': _cfg( url='https://storage.googleapis.com/vit_models/augreg/' 'B_16-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.1-sd_0.1.npz'),
• For Swin-Transformer: Please refer to https://github.com/microsoft/Swin-Transformer for the pretrained models, download the model and put it at --pretrained_dir folder

3. Train Model (refer to additional notes for usage of more tags)

• ViT-CWT train on Split-2, KS-0.65 of Cifar-10 and real world federated dataset CelebA

python train_CWT.py --FL_platform ViT-CWT --net_name ViT-small --dataset cifar10 --E_epoch 1 --max_communication_rounds 100 --split_type split_2 --save_model_flag
python train_CWT.py --FL_platform ViT-CWT --net_name ViT-small --dataset CelebA --E_epoch 1 --max_communication_rounds 30 --split_type real

• ViT-FedAVG train on Split-2, KS-0.65 of Cifar-10 and real world federated dataset CelebA

python train_FedAVG.py --FL_platform ViT-FedAVG --net_name ViT-small --dataset cifar10 --E_epoch 1 --max_communication_rounds 100 --num_local_clients -1 --split_type split_2 --save_model_flag
python train_FedAVG.py --FL_platform ViT-FedAVG --net_name ViT-small --dataset CelebA --E_epoch 1 --max_communication_rounds 30 --num_local_clients 10 --split_type real

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

Additional Notes

• Some important tags for both train_CWT.py and train_FedAVG.py:

  • --FL_platform: selection of FL platforms, ViT-CWT, ResNet-CWT, EfficientNet-CWT, or Swin-CWT for train_CWT.py, ViT-FedAVG, ResNet-FedAVG, EfficientNet-FedAVG, or Swin-FedAVG for train_FedAVG.py
  • --net_name: basic Name of this run, also providing detailed network-architecture for ViT/ResNet/EfficientNet. For ViT: ViT-small, ViT-tiny, ViT-base(default), For EfficientNet: efficientnet-b1, efficientnet-b5(default), efficientnet-b7 see sstart_config.py for more details
  • --dataset: choose of the following three datasets ["cifar10", "Retina" ,"CelebA"]
  • --save_model_flag: set to True if need to save the checkpoints
  • --output_dir: the output directory where checkpoints/results/logs will be written
  • --decay_type: learning rate decay schedulers with the following three options ["cosine", "linear", "step"]
  • --E_epoch: local training epoch E in FL train
  • --max_communication_rounds: total communication rounds, 100 for Retina and Cifar-10, 30 for CelebA
  • --split_type: type of data partitions, supports ["split_1", "split_2", "split_3"] for Cifar-10 and Retina, ["real"] for CelebA
  • --cfg: configuration document for Swin-transformers if use Swin-FL, otherwise ignored it

• Additional tag for paralle FedAVG

  • --num_local_clients: Num of local clients joined in each FL train. -1 (usage of all local clients) for Retina and Cifar-10, 10 for CelebA.

• Also refer to the train_CWT.py and train_FedAVG.py for more tags

Acknowledgments

• Our Swin-Transformer implementation is based on Pytorch Swin implementation
• ViT implementation is based on https://github.com/rwightman/pytorch-image-models
• Original ViT implementation at Google ViT