Federated Averaging

Requirements

Make sure GPU is avaible and CUDA>=11.0 has been installed on your computer. You can check it with
```
    nvidia-smi
```

Download the repo with following command:

    git clone --recurse-submodules https://github.com/MrZhang1994/mobile-federated-learning.git

Follow the instruction or documentation of FedML to install required package in python environment. Or, you can simply create an virtural environment with python>=3.8 and run pip install -r requirements.txt to download the required packages. If you use anaconda3 or miniconda, you can run following instructions to download the required packages in python.
```
    conda create -y -n mfl python=3.8
    conda activate mfl
    pip install pip --upgrade
    pip install -r requirements.txt
```

Experimental Tracking Platform

report real-time result to wandb.com, please change ID to your own

wandb login `Your ID`

Experiment Scripts

To test whether program are correctly configured, you can run following commands to see whether training process starts correctly.

sh begin.sh -[VERBOSE]

Or, you can try other heterogeneous distribution (Non-IID) experiment:

## MNIST (non-i.i.d)
sh run_fedavg_standalone_pytorch.sh 0 mnist ../FedML/data/MNIST lr hetero 59361 0.03 sgd sch_random -v

## shakespeare (non-i.i.d LEAF)
sh run_fedavg_standalone_pytorch.sh 0 shakespeare ../FedML/data/shakespeare rnn hetero 59361 0.03 sgd sch_random -v

# fed_shakespeare (non-i.i.d Google)
sh run_fedavg_standalone_pytorch.sh 0 fed_shakespeare ../FedML/data/fed_shakespeare rnn hetero 59361 0.03 sgd sch_random -v

## Federated EMNIST (non-i.i.d)
sh run_fedavg_standalone_pytorch.sh 0 femnist ../FedML/data/FederatedEMNIST/datasets cnn hetero 59361 0.03 sgd sch_random -v

## Fed_CIFAR100 (non-i.i.d)
sh run_fedavg_standalone_pytorch.sh 0 fed_cifar100 ../FedML/data/fed_cifar100 resnet18_gn hetero 59361 0.03 adam sch_random -v

# Stackoverflow_LR (non-i.i.d)
sh run_fedavg_standalone_pytorch.sh 0 stackoverflow_lr ../FedML/data/stackoverflow lr hetero 59361 0.03 sgd sch_random -v

# Stackoverflow_NWP (non-i.i.d)
sh run_fedavg_standalone_pytorch.sh 0 stackoverflow_nwp ../FedML/data/stackoverflow rnn hetero 59361 0.03 sgd sch_random -v
 
# CIFAR10 (non-i.i.d) 
sh run_fedavg_standalone_pytorch.sh 0 cifar10 ../FedML/data/cifar10 resnet56 hetero 59361 0.03 sgd sch_random -v

All above datasets are heterogeneous non-i.i.d distributed.

Clean Partial Results

There might be some partial results generated during training which are stored in following directories, including ./src/wandb, ./src/__pycache, ./src/runs and ./src/result. They may not be used anymore. If you want to remove them from the current workspace and make sure to backup the dir ./src/result which stores almost all the information of training process to dir ./src/outs, you can simply run the following instruction.

    sh clean.sh

Or, you want to specify the name of the backup file, you can run

    sh clean.sh [NAME]

One usage example can be shown by run sh clean.sh help.

Benchmark Results