Federated Learning

Reference

Communication-Efficient Learning of Deep Networks from Decentralized Data

This is partly the reproduction of the paper of Communication-Efficient Learning of Deep Networks from Decentralized Data
Only experiments on MNIST and CIFAR10 (both IID and non-IID) is produced by far.

Note: The scripts will be slow without the implementation of parallel computing.

Federated Learning on Non-IID Data with Local-drift Decoupling and Correction Code for paper - [Federated Learning on Non-IID Data with Local-drift Decoupling and Correction]

We provide code to run FedDC, FedAvg, FedDyn, Scaffold, and FedProx methods.

HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

This is the PyTorch implemention of our paper HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images by Meirui Jiang, Zirui Wang and Qi Dou.

FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views

https://arxiv.org/abs/2212.02196

FedTP: Federated Learning by Transformer Personalization

Requirements

torch 2.0.1
Python 3.10.11

Env.

Build cython file

build cython file for amplitude normalization

python utils/setup.py build_ext --inplace

Run

The MLP and CNN models are produced by:

python main_nn.py

Federated learning with MLP and CNN is produced by:

python main_fed.py

See the arguments in options.py.

For example:

python main_fed.py --dataset mnist --iid --num_channels 1 --model cnn --epochs 50 --gpu 0

--all_clients for averaging over all client models

NB: for CIFAR-10, num_channels must be 3.

Results

MNIST

Results are shown in Table 1 and Table 2, with the parameters C=0.1, B=10, E=5.

Table 1. results of 10 epochs training with the learning rate of 0.01

Model	Acc. of IID	Acc. of Non-IID
FedAVG-MLP	94.57%	70.44%
FedAVG-CNN	96.59%	77.72%

Table 2. results of 50 epochs training with the learning rate of 0.01

Model	Acc. of IID	Acc. of Non-IID
FedAVG-MLP	97.21%	93.03%
FedAVG-CNN	98.60%	93.81%

Ackonwledgements

Acknowledgements give to youkaichao.

References

McMahan, Brendan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Artificial Intelligence and Statistics (AISTATS), 2017.

Cite As

Shaoxiong Ji. (2018, March 30). A PyTorch Implementation of Federated Learning. Zenodo. http://doi.org/10.5281/zenodo.4321561

CMD

# MNIST CNN MLP FEDAVG HARMOFL
python main_nn.py --dataset mnist --iid --num_channels 1 --model cnn --epochs 1 --gpu 0 

python main_nn.py --dataset mnist --iid --num_channels 1 --model cnn --epochs 1 --gpu 0 --all_clients

python main_nn.py --dataset mnist --iid --num_channels 1 --model mlp --epochs 1 --gpu 0 

python main_nn.py --dataset mnist --iid --num_channels 1 --model mlp --epochs 1 --gpu 0 --all_clients

# MNIST NN FEDAVG HARMOFL
python main_nn.py --dataset mnist --iid --num_channels 1 --model 2nn --epochs 1 --gpu 0  

python main_nn.py --dataset mnist --iid --num_channels 1 --model 2nn --epochs 1 --gpu 0 --all_clients

# CIFAR10 CNN MLP FEDAVG HARMOFL
python main_nn.py --dataset cifar --iid --num_channels 3 --model cnn --epochs 1 --gpu 0

python main_nn.py --dataset cifar --iid --num_channels 3 --model cnn --epochs 1 --gpu 0 --all_clients

python main_nn.py --dataset cifar --iid --num_channels 3 --model mlp --epochs 1 --gpu 0

python main_nn.py --dataset cifar --iid --num_channels 3 --model mlp --epochs 1 --gpu 0 --all_clients

# CIFAR100 CNN MLP FEDAVG HARMOFL
python main_nn.py --dataset cifar100 --iid --num_channels 3 --model cnn --epochs 1 --gpu 0 --num_classes 100

python main_nn.py --dataset cifar100 --iid --num_channels 3 --model cnn --epochs 1 --gpu 0 --num_classes 100 --all_clients

python main_nn.py --dataset cifar100 --iid --num_channels 3 --model mlp --epochs 1 --gpu 0 --num_classes 100

python main_nn.py --dataset cifar100 --iid --num_channels 3 --model mlp --epochs 1 --gpu 0 --num_classes 100 --all_clients

# EMNIST NN FEDAVG HARMOFL
python main_nn.py --dataset emnist --iid --num_channels 1 --model nn --epochs 1 --gpu 0

python main_nn.py --dataset emnist --iid --num_channels 1 --model nn --epochs 1 --gpu 0 --all_clients

# SALT UNET FEDAVG HARMOFL
python main_nn.py --dataset salt --iid --num_channels 1 --model unet --epochs 1 --gpu 0

python main_nn.py --dataset salt --iid --num_channels 1 --model unet --epochs 1 --gpu 0 --all_clients

Fed Test.

# MNIST CNN MLP FEDAVG HARMOFL
python main_fed.py --dataset mnist --iid --num_channels 1 --model cnn --epochs 1 --gpu 0 --methods harmofl

python main_fed.py --dataset mnist --iid --num_channels 1 --model cnn --epochs 1 --gpu 0 --methods harmofl --all_clients

python main_fed.py --dataset mnist --iid --num_channels 1 --model cnn --epochs 1 --gpu 0 

python main_fed.py --dataset mnist --iid --num_channels 1 --model cnn --epochs 1 --gpu 0 --all_clients

python main_fed.py --dataset mnist --iid --num_channels 1 --model mlp --epochs 1 --gpu 0 --methods harmofl

python main_fed.py --dataset mnist --iid --num_channels 1 --model mlp --epochs 1 --gpu 0 --methods harmofl --all_clients

python main_fed.py --dataset mnist --iid --num_channels 1 --model mlp --epochs 1 --gpu 0 

python main_fed.py --dataset mnist --iid --num_channels 1 --model mlp --epochs 1 --gpu 0 --all_clients

# MNIST NN FEDAVG HARMOFL
python main_fed.py --dataset mnist --iid --num_channels 1 --model 2nn --epochs 1 --gpu 0  

python main_fed.py --dataset mnist --iid --num_channels 1 --model 2nn --epochs 1 --gpu 0 --all_clients

python main_fed.py --dataset mnist --iid --num_channels 1 --model 2nn --epochs 1 --gpu 0 --methods harmofl 

python main_fed.py --dataset mnist --iid --num_channels 1 --model 2nn --epochs 1 --gpu 0 --methods harmofl --all_clients

# CIFAR10 CNN MLP FEDAVG HARMOFL
python main_fed.py --dataset cifar --iid --num_channels 3 --model cnn --epochs 1 --gpu 0

python main_fed.py --dataset cifar --iid --num_channels 3 --model cnn --epochs 1 --gpu 0 --all_clients

python main_fed.py --dataset cifar --iid --num_channels 3 --model cnn --epochs 1 --gpu 0 --methods harmofl 

python main_fed.py --dataset cifar --iid --num_channels 3 --model cnn --epochs 1 --gpu 0  --methods harmofl --all_clients

python main_fed.py --dataset cifar --iid --num_channels 3 --model mlp --epochs 1 --gpu 0

python main_fed.py --dataset cifar --iid --num_channels 3 --model mlp --epochs 1 --gpu 0 --all_clients

python main_fed.py --dataset cifar --iid --num_channels 3 --model mlp --epochs 1 --gpu 0 --methods harmofl 

python main_fed.py --dataset cifar --iid --num_channels 3 --model mlp --epochs 1 --gpu 0 --methods harmofl --all_clients

# CIFAR100 CNN MLP FEDAVG HARMOFL
python main_fed.py --dataset cifar100 --iid --num_channels 3 --model cnn --epochs 1 --gpu 0 --num_classes 100

python main_fed.py --dataset cifar100 --iid --num_channels 3 --model cnn --epochs 1 --gpu 0 --num_classes 100 --all_clients

python main_fed.py --dataset cifar100 --iid --num_channels 3 --model cnn --epochs 1 --gpu 0 --num_classes 100 --methods harmofl 

python main_fed.py --dataset cifar100 --iid --num_channels 3 --model cnn --epochs 1 --gpu 0 --num_classes 100 --methods harmofl --all_clients

python main_fed.py --dataset cifar100 --iid --num_channels 3 --model mlp --epochs 1 --gpu 0 --num_classes 100

python main_fed.py --dataset cifar100 --iid --num_channels 3 --model mlp --epochs 1 --gpu 0 --num_classes 100 --all_clients

python main_fed.py --dataset cifar100 --iid --num_channels 3 --model mlp --epochs 1 --gpu 0 --num_classes 100 --methods harmofl 

python main_fed.py --dataset cifar100 --iid --num_channels 3 --model mlp --epochs 1 --gpu 0 --num_classes 100 --methods harmofl --all_clients

# EMNIST NN FEDAVG HARMOFL
python main_fed.py --dataset emnist --iid --num_channels 1 --model nn --epochs 1 --gpu 0

python main_fed.py --dataset emnist --iid --num_channels 1 --model nn --epochs 1 --gpu 0 --all_clients

python main_fed.py --dataset emnist --iid --num_channels 1 --model nn --epochs 1 --gpu 0 --methods harmofl

python main_fed.py --dataset emnist --iid --num_channels 1 --model nn --epochs 1 --gpu 0 --methods harmofl --all_clients

# SALT UNET FEDAVG HARMOFL
python main_fed.py --dataset salt --iid --num_channels 1 --model unet --epochs 1 --gpu 0

python main_fed.py --dataset salt --iid --num_channels 1 --model unet --epochs 1 --gpu 0 --all_clients

python main_fed.py --dataset salt --iid --num_channels 1 --model unet --epochs 1 --gpu 0 --method harmofl

python main_fed.py --dataset salt --iid --num_channels 1 --model unet --epochs 1 --gpu 0 --method harmofl --all_clients

kancheng/fed-unet-init-test