GalaxyDataset

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A modular evaluation metrics and a benchmark for large-scale federated learning

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# Non-IID Dataset and Evaluation Metrics For Federated Learning

Federated learning is proposed as a machine learning setting to enable distributed edge devices (e.g., mobile phones) to collaboratively learn a shared model while keeping all the training data on devices, which can not only take full advantage of data distributed across millions of nodes but also protect data privacy. However, learning in the federated scenario above poses new challenges. One of the most prominent challenges is that the data distribution in different nodes violates the assumption of independent and identically distribution (IID) of data in traditional deep learning. Therefore, the non-independent and identically distributed (Non-IID) dataset becomes a research topic in federated learning. In this paper, we propose a modular benchmarking framework which includes three parts of work: 1) a mathematical equation to measure the distribution of different datasets, 2) a suite of customizable solutions to generate non-IID datasets, 3) a set of evaluation metrics to evaluate the performance of machine learning algorithms. Moreover, a set of practical non-IID benchmarkable datasets will also be released.

## Framework

Non-IID Dataset and Evaluation Metrics Paradigm is:

## Non-IID Dataset Module

### Requirements

requirements.txt

```yaml
matplotlib==3.1.1
pandas==0.25.0
pyparsing==2.4.1.1
pyrsistent==0.15.3
python-dateutil==2.8.0
torch==1.2.0
torchvision==0.4.0
yaml
```

Environment: python3.6

```python
pip3 install -r requirements.txt
```

### Non-IID Equation Definition

Adding a standard to measure the degree of non independence and distribution of data. Using NI,

Given a feature extractor ![](https://latex.codecogs.com/svg.latex?g_{\varphi}(\cdot)) and a class C, the degree of distribution shift is ![](https://latex.codecogs.com/svg.latex?D_{test}^{C})   deﬁned as:

![](https://latex.codecogs.com/svg.latex?NEI(C)=\left\|\frac{\overline{En\left(X_{\text{i}}^{C}\right)}-\overline{En\left(X_{\text{j}}^{C}\right)}}{\sigma\left(En\left(X^{C}\right)\right)}\right\|_{2})

where ![](https://latex.codecogs.com/svg.latex?X^{C}=X_{i}^{C}%20\cup%20X_{j}^{C}).

#### Usage

step1: download datasets

step2: run NEI.py with datasets

step3: get results

### Non-IID Dataset Generation

According to number of nodes, we split CIFAR10 dataset. We can customize the config.yaml configuration file. Set the split mode, randomly or by category. Set the number of nodes, the size of the data set for each node, and the number of each node category. 
In addition, we can also set the same distribution of data sets to increase the error dataset.

In this part, we use `downloadData.py 、makeDataset.py and preprocess.py` to generate non-IID datasets. More importantly, we provide a config file config.yaml for setting related parameters about non-IID datasets. We now work on MNIST and CIFAR10 datasets.

#### In config.yaml:

We will create non-IID dataset based on the number of nodes;

In config.yaml file: we will create non-IID dataset based on the number of nodes;

We will set  node_num  parameter which represents the number of dataset we want to generate. For example, we want 500 dataset with 500nodes. We will set node_num: 500.

```
# dataset
dataset_mode: MNIST/ CIFAR10

# node num  Number of node (default n=10) one node corresponding to one dataset,
node_num: 4

# small dataset config
isaverage_dataset_size: false # if average splits dataset
# numpy.random.randint(10000, 20000 , 20)
#dataset_size_list: [18268, 16718, 10724, 12262, 17094, 14846, 17888, 14273, 13869,
##                    18087, 19631, 15390, 14346, 12743, 11246, 18375, 15813, 18280,
##                    12300, 12190]
dataset_size_list: [1822, 1000, 1200, 1300]

# split mode
split_mode: 0 # dataset split: randomSplit(0), splitByLabels(1)

# each node - label kind , sum must <= 10
# numpy.random.randint(0, 11, 20)
node_label_num: [ 4,  3,  2, 10]

isadd_label: false
add_label_rate: 0.1

isadd_error: false
add_error_rate: 0.01
```

We will generate n custom datasets.s

#### In downloadData.py:

We can download differents datasets.

#### In makeDataset.py:

In this part, we provide different methods for partitioning dataset.

1. randomSplit: 
    1) no error dataset 
    2) add error dataset
2. splitByLabel: 
    1. just split dataset
    2. add other dataset, no error dataset
    3. add error dataset, no other dataset 
    4. add both dataset
3. redefineLabels

We will generate n custom datasets.
On cifar10 files, every npy file consists of python's List.

```
npy file: [[imgs, label], [imgs, label]...., [imgs, label]]
```

We can see the name of each npy file:

```
npy file name: split node index mode_dataset size_target label_the ratio of other label_the ratio of error dataset
```

#### In preprocess.py:

We will use readnpy method to read npy file

```python
def readnpy(path):
    # npy file: [[imgs, label], [imgs, label]...., [imgs, label]]
    # when allow_pickle=True, matrix needs same size
    np_array = np.load(path, allow_pickle=True)
    imgs = []
    label = []
    for index in range(len(np_array)):
        imgs.append(np_array[index][0])
        label.append(np_array[index][1])
    torch_dataset = Data.TensorDataset(torch.from_numpy(np.array(imgs)), torch.from_numpy(np.array(label)))

dataloader = Data.DataLoader(
        torch_dataset,
        batch_size=64,
        shuffle=True
    )
    print(dataloader)
    return dataloader
dataloader = readnpy("./cifar10/splitByLabelsWithNormalAndErrorDataset/SplitByLabels_2222_horseandMore_0.1_0.01.npy")
```

## Evaluation Metrics

**Data Nodes Number**: In federated learning, it is an unavoidable learning process that multiple nodes participate in learning to get a global model.

**The Communication Rounds**:  There is no doubt that the communication rounds of nodes play an important role in the performance of the model.

**The Weight of Data Nodes**: We need to recognizes the importance of specifying how the accuracy is weighted across nodes, e.g., whether every node is equally important, or every data node equally important (implying that the more data, the more important the node).

**The Quality of Data Nodes**:  In this part, we research the influence of the same distributed data or the same data proportion in the total data and the wrong data proportion in the total data proportion on the performance of the model. We regard the above properties as the quality of data.

## Benchmark

![image-20191227023138927](https://raw.githubusercontent.com/ZJU-DistributedAI/GalaxyDataset/master/resources/images/2.png)

![image-20191227023203627](https://raw.githubusercontent.com/ZJU-DistributedAI/GalaxyDataset/master/resources/images/3.png)

GalaxyDataset

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