NIID-Bench

This is the code of paper Federated Learning on Non-IID Data Silos: An Experimental Study.

This code runs a benchmark for federated learning algorithms under non-IID data distribution scenarios. Specifically, we implement 4 federated learning algorithms (FedAvg, FedProx, SCAFFOLD & FedNova), 3 types of non-IID settings (label distribution skew, feature distribution skew & quantity skew) and 9 datasets (MNIST, Cifar-10, Fashion-MNIST, SVHN, Generated 3D dataset, FEMNIST, adult, rcv1, covtype).

Non-IID Settings

Label Distribution Skew

Quantity-based label imbalance: each party owns data samples of a fixed number of labels.
Distribution-based label imbalance: each party is allocated a proportion of the samples of each label according to Dirichlet distribution.

Feature Distribution Skew

Noise-based feature imbalance: We first divide the whole datasetinto multiple parties randomly and equally. For each party, we adddifferent levels of Gaussian noises.
Synthetic feature imbalance: For generated 3D data set, we allocate two parts which are symmetric of(0,0,0) to a subset for each party.
Real-world feature imbalance: For FEMNIST, we divide and assign thewriters (and their characters) into each party randomly and equally.

Quantity Skew

While the data distribution may still be consistent amongthe parties, the size of local dataset varies according to Dirichlet distribution.

Usage

Here is one example to run this code:

python experiments.py --model=simple-cnn \
    --dataset=cifar10 \
    --alg=fedprox \
    --lr=0.01 \
    --batch-size=64 \
    --epochs=10 \
    --n_parties=10 \
    --mu=0.01 \
    --rho=0.9 \
    --comm_round=50 \
    --partition=noniid-labeldir \
    --beta=0.5\
    --device='cuda:0'\
    --datadir='./data/' \
    --logdir='./logs/' \
    --noise=0 \
    --sample=1 \
    --init_seed=0

Parameter	Description
`model`	The model architecture. Options: `simple-cnn`, `vgg`, `resnet`, `mlp`. Default = `mlp`.
`dataset`	Dataset to use. Options: `mnist`, `cifar10`, `fmnist`, `svhn`, `generated`, `femnist`, `a9a`, `rcv1`, `covtype`. Default = `mnist`.
`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 = `64`.
`epochs`	Number of local training epochs, default = `5`.
`n_parties`	Number of parties, default = `2`.
`mu`	The proximal term parameter for FedProx, default = `1`.
`rho`	The parameter controlling the momentum SGD, default = `0`.
`comm_round`	Number of communication rounds to use, default = `50`.
`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 = `homo`
`beta`	The concentration parameter of the Dirichlet distribution for heterogeneous partition, default = `0.5`.
`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/`.
`noise`	Maximum variance of Gaussian noise we add to local party, default = `0`.
`sample`	Ratio of parties that participate in each communication round, default = `1`.
`init_seed`	The initial seed, default = `0`.

Data Partition Map

You can call function get_partition_dict() in experiments.py to access net_dataidx_map. net_dataidx_map is a dictionary. Its keys are party ID, and the value of each key is a list containing index of data assigned to this party. For our experiments, we usually set init_seed=0. When we repeat experiments of some setting, we change init_seed to 1 or 2. The default value of noise is 0 unless stated. We list the way to get our data partition as follow.

Quantity-based label imbalance: partition=noniid-#label1, noniid-#label2 or noniid-#label3
Distribution-based label imbalance: partition=noniid-labeldir, beta=0.5 or 0.1
Noise-based feature imbalance: partition=homo, noise=0.1 (actually noise does not affect net_dataidx_map)
Synthetic feature imbalance & Real-world feature imbalance: partition=real
Quantity Skew: partition=iid-diff-quantity, beta=0.5 or 0.1
IID Setting: partition=homo
Mixed skew: partition = mixed for mixture of distribution-based label imbalance and quantity skew; partition = noniid-labeldir and noise = 0.1 for mixture of distribution-based label imbalance and noise-based feature imbalance.

Here is explanation of parameter for function get_partition_dict().

Parameter	Description
`dataset`	Dataset to use. Options: `mnist`, `cifar10`, `fmnist`, `svhn`, `generated`, `femnist`, `a9a`, `rcv1`, `covtype`.
`partition`	Tha 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`
`n_parties`	Number of parties.
`init_seed`	The initial seed.
`datadir`	The path of the dataset.
`logdir`	The path to store the logs.
`beta`	The concentration parameter of the Dirichlet distribution for heterogeneous partition.

Leader Board

Note that the accuracy shows the average of three experiments, while the training curve is based on only one experiment. Thus, there may be some difference. We show the training curve to compare convergence rate of different algorithms.

Quantity-based label imbalance

Cifar-10, 10 parties, sample rate = 1, batch size = 64, learning rate = 0.01

Partition	Model	Round	Algorithm	Accuracy
`noniid-#label2`	`simple-cnn`	50	FedProx (`mu=0.01`)	50.7%
`noniid-#label2`	`simple-cnn`	50	FedAvg	49.8%
`noniid-#label2`	`simple-cnn`	50	SCAFFOLD	49.1%
`noniid-#label2`	`simple-cnn`	50	FedNova	46.5%

Cifar-10, 100 parties, sample rate = 0.1, batch size = 64, learning rate = 0.01

Partition	Model	Round	Algorithm	Accuracy
`noniid-#label2`	`simple-cnn`	500	FedNova	48.0%
`noniid-#label2`	`simple-cnn`	500	FedAvg	45.3%
`noniid-#label2`	`simple-cnn`	500	FedProx (`mu=0.001`)	39.3%
`noniid-#label2`	`simple-cnn`	500	SCAFFOLD	10.0%

Distribution-based label imbalance

Cifar-10, 10 parties, sample rate = 1, batch size = 64, learning rate = 0.01

Partition	Model	Round	Algorithm	Accuracy
`noniid-labeldir` with `beta=0.5`	`simple-cnn`	50	SCAFFOLD	69.8%
`noniid-labeldir` with `beta=0.5`	`simple-cnn`	50	FedAvg	68.2%
`noniid-labeldir` with `beta=0.5`	`simple-cnn`	50	FedProx (`mu=0.001`)	67.9%
`noniid-labeldir` with `beta=0.5`	`simple-cnn`	50	FedNova	66.8%

Partition	Model	Round	Algorithm	Accuracy
`noniid-labeldir` with `beta=0.1`	`vgg`	100	SCAFFOLD	85.5%
`noniid-labeldir` with `beta=0.1`	`vgg`	100	FedNova	84.4%
`noniid-labeldir` with `beta=0.1`	`vgg`	100	FedProx (`mu=0.01`)	84.4%
`noniid-labeldir` with `beta=0.1`	`vgg`	100	FedAvg	84.0%

Cifar-10, 100 parties, sample rate = 0.1, batch size = 64, learning rate = 0.01

Partition	Model	Round	Algorithm	Accuracy
`noniid-labeldir` with `beta=0.5`	`simple-cnn`	500	FedNova	60.0%
`noniid-labeldir` with `beta=0.5`	`simple-cnn`	500	FedAvg	59.4%
`noniid-labeldir` with `beta=0.5`	`simple-cnn`	500	FedProx (`mu=0.001`)	58.8%
`noniid-labeldir` with `beta=0.5`	`simple-cnn`	500	SCAFFOLD	10.0%

Noise-based feature imbalance

Cifar-10, 10 parties, sample rate = 1, batch size = 64, learning rate = 0.01

Partition	Model	Round	Algorithm	Accuracy
`homo` with `noise=0.1`	`simple-cnn`	50	SCAFFOLD	70.1%
`homo` with `noise=0.1`	`simple-cnn`	50	FedProx (`mu=0.01`)	69.3%
`homo` with `noise=0.1`	`simple-cnn`	50	FedAvg	68.9%
`homo` with `noise=0.1`	`simple-cnn`	50	FedNova	68.5%

Partition	Model	Round	Algorithm	Accuracy
`homo` with `noise=0.1`	`resnet`	100	SCAFFOLD	90.2%
`homo` with `noise=0.1`	`resnet`	100	FedNova	89.4%
`homo` with `noise=0.1`	`resnet`	100	FedProx (`mu=0.01`)	89.2%
`homo` with `noise=0.1`	`resnet`	100	FedAvg	89.1%

Quantity Skew

Cifar-10, 10 parties, sample rate = 1, batch size = 64, learning rate = 0.01

Partition	Model	Round	Algorithm	Accuracy
`iid-diff-quantity` with `beta=0.5`	`simple-cnn`	50	FedAvg	72.0%
`iid-diff-quantity` with `beta=0.5`	`simple-cnn`	50	FedProx (`mu=0.01`)	71.2%
`iid-diff-quantity` with `beta=0.5`	`simple-cnn`	50	SCAFFOLD	62.4%
`iid-diff-quantity` with `beta=0.5`	`simple-cnn`	50	FedNova	10.0%

IID Setting

Cifar-10, 100 parties, sample rate = 0.1, batch size = 64, learning rate = 0.01

Partition	Model	Round	Algorithm	Accuracy
`homo`	`simple-cnn`	500	FedNova	66.1%
`homo`	`simple-cnn`	500	FedProx (`mu=0.01`)	66.0%
`homo`	`simple-cnn`	500	FedAvg	65.6%
`homo`	`simple-cnn`	500	SCAFFOLD	10.0%

Citation

If you find this repository useful, please cite our paper:

@article{li2021federated,
      title={Federated Learning on Non-IID Data Silos: An Experimental Study},
      author={Li, Qinbin and Diao, Yiqun and Chen, Quan and He, Bingsheng},
      journal={arXiv preprint arXiv:2102.02079}
      year={2021}
}

kimcando/NIID-Bench

NIID-Bench

Non-IID Settings

Label Distribution Skew

Feature Distribution Skew

Quantity Skew

Usage

Data Partition Map

Leader Board

Quantity-based label imbalance

Distribution-based label imbalance

Noise-based feature imbalance

Quantity Skew

IID Setting

Citation