mediabrain-sjtu / fedmr

[TMLR 2023]Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping

FedMR

[TMLR 2023]Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping

Dependencies

• PyTorch >= 1.0.0
• torchvision >= 0.2.1
• scikit-learn >= 0.23.1

Data Preparing

Here we provide the implementation on SVHN, Cifar10 and Cifar100 datasets. The three datasets will be automatically downloaded in your datadir.

Model Structure

As for model used in the paper, we use the same model structure of SimpleCNN and ResNet18 modified for 32x32 input as MOON.

Parameters

Parameter	Description
model	The model architecture. Options: simple-cnn, resnet18.
dataset	Dataset to use. Options: CIFAR10. CIFAR100, SVHN
lr	Learning rate.
batch-size	Batch size.
epochs	Number of local epochs.
n_parties	Number of parties.
party_per_round	number of active clients in each round.
comm_round	Number of communication rounds.
beta	The concentration parameter of the Dirichlet distribution for non-IID partition. Setting 100000 as IID
datadir	The path of the dataset.
logdir	The path to store the logs.
seed	The initial seed.
mu	Param of baselines.

Usage

Here is an example to run FedGELA on CIFAR10 with ResNet18:

python FedMR.py --dataset=cifar10 \
    --partition='dirichlet' \
    --lr=0.01 \
    --epochs=10 \
    --model=resnet18 \
    --comm_round=100 \
    --n_parties=50 \
    --beta=0.5 \
    --party_per_round=10 \
    --logdir='./logs/' \
    --datadir='./data/' \
    --mu=0.00001 \

Acknowledgement

We borrow some codes from MOON and FedSkip.

Contact

If you have any problem with this code, please feel free to contact [email protected] or [email protected].