This is the code for paper(ID:3669) [FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation].

Dependencies

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

Parameters

Parameter	Description
skip	Number of skip between two aggregations.
model	The model architecture. Options: simple-cnn, resnet50 .
dataset	Dataset to use. Options: cifar10. cifar100, femnist
lr	Learning rate.
batch-size	Batch size.
epochs	Number of local epochs.
n_parties	Number of parties.
sample_fraction	the fraction of parties to be sampled in each round.
comm_round	Number of communication rounds.
partition	The partition approach. Options: noniid, iid.
beta	The concentration parameter of the Dirichlet distribution for non-IID partition.
datadir	The path of the dataset.
logdir	The path to store the logs.
device	Specify the device to run the program.
seed	The initial seed.

For Cifar-10 and FEMNIST, you should use simple-cnn while for CIFAR-100, you should use resnet50. You can set beta as large as possible to simulate IID when partition=non-iid. We set lr=0.01, epochs=10 and batch-size=64 by default in the paper.

Usage

Here is an example to run FedSkip-3 on CIFAR-10 with a simple CNN:

python main.py --dataset=cifar10 \
    --model=simple-cnn \
    --skip=3 \
    --lr=0.01 \
    --epochs=10 \
    --comm_round=100 \
    --n_parties=10 \
    --partition=noniid \
    --beta=0.5 \
    --sample_fraction=1.0 \
    --logdir='./logs/' \
    --datadir='./data/' \