NIID-Bench

This is the code of paper 《一种面向智联网的高效联邦学习》.

This code runs a benchmark for federated learning algorithms under non-IID data distribution scenarios. Specifically, we implement 6 federated learning algorithms (FedAvg, FedProx, fedavgm,fedadam & Fedcnm).

Usage

Here is one example to run this code:

python experiments.py --model=resnet \
    --dataset=cifar10 \
    --alg=fedprox \
    --lr=0.01 \
    --batch-size=32 \
    --epochs=1 \
    --n_parties=100 \
    --mu=0.01 \
    --rho=0.9 \
    --comm_round=500 \
    --partition=noniid-labeldir \
    --beta=0.5\
    --device='cuda:0'\
    --datadir='./data/' \
    --logdir='./logs/' \
    --sample=0.1 \
    --init_seed=0

Parameter	Description
model	The model architecture. Options: simple-cnn, vgg, resnet, mlp,resnet. Default = resnet.
dataset	Dataset to use. Options: mnist, cifar10,cifar100, fmnist, svhn, generated, femnist, a9a, rcv1, covtype. Default = cifar10.
alg	The training algorithm. Options: fedavg, fedprox, scaffold, fednova. Default = fedavg.
lr	Learning rate for the local models, default = 0.01.
batch-size	Batch size, default = 32.
epochs	Number of local training epochs, default = 1.
n_parties	Number of parties, default = 100.
mu	The proximal term parameter for FedProx, default = 0.001.
rho	The parameter controlling the momentum SGD, default = 0.
comm_round	Number of communication rounds to use, default = 500.
partition	The partition way. Options: homo, noniid-labeldir, noniid-#label1 (or 2, 3, ..., which means the fixed number of labels each party owns), real, iid-diff-quantity. Default = noniid-labeldir
beta	The concentration parameter of the Dirichlet distribution for heterogeneous partition, default = 0.3.
device	Specify the device to run the program, default = cuda:0.
datadir	The path of the dataset, default = ./data/.
logdir	The path to store the logs, default = ./logs/.
sample	Ratio of parties that participate in each communication round, default = 0.1.
init_seed	The initial seed, default = 0.