Federated-Learning (PyTorch)

Experiments are produced on MNIST, Fashion MNIST and CIFAR10 (IID).

Since the purpose of these experiments are to illustrate the effectiveness of the federated learning paradigm, models such as MLP and CNN are used.

Requirments

• Python3
• Pytorch
• Torchvision
• Numpy
• Pandas
• Matplotlib
• Scikit-learn
• Scipy
• TensorboardX
• tqdm

Data

• Download train and test datasets manually or they will be automatically downloaded from torchvision datasets.
• Experiments are run on Mnist, Fashion Mnist and Cifar.
• To use your own dataset: Move your dataset to data directory and write a wrapper on pytorch dataset class.

Running the experiments

The Federated_main trains the model in the conventional way.

• To run the baseline experiment with MNIST on MLP using CPU:

python src/baseline_main.py --model=mlp --dataset=mnist --epochs=10

• To run it on GPU (eg: if gpu:0 is available):

python src/baseline_main.py --model=mlp --dataset=mnist --gpu=0 --epochs=10

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Federated experiment involves training a global model using many local models.

• To run the federated experiment with CIFAR on CNN (IID):

python src/federated_main.py --model=cnn --dataset=cifar --gpu=0 --iid=1 --epochs=10

If you want to change the default values of other parameters to simulate different conditions. Refer to the options section.

Options

The default values for various paramters parsed to the experiment are given in options.py. Details are given some of those parameters:

• --dataset: Default: 'mnist'. Options: 'mnist', 'fmnist', 'cifar'
• --model: Default: 'mlp'. Options: 'mlp', 'cnn'
• --gpu: Default: None (runs on CPU). Can also be set to the gpu id.
• --epochs: Number of rounds of training.
• --lr: Learning rate set to 0.01 by default.
• --verbose: Detailed log outputs. Activated by default, set to 0 to deactivate.
• --seed: Random Seed. Default set to 1.

Federated Parameters

• --iid: Distribution of data amongst users. Default set to IID. Set to 0 for non-IID.
• --num_users:Number of users. Default is 100.
• --frac: Fraction of users to be used for federated updates. Default is 0.1.
• --local_ep: Number of local training epochs in each user. Default is 10.
• --local_bs: Batch size of local updates in each user. Default is 10.