Federated Averaging

Requirements

Download the repo on Github FedML

Change the root dir in main_fedavg.py to the absolute path of FedML. For example,

    sys.path.insert(0, os.path.abspath("/home/zzp1012/FedML")) # add the root dir of FedML

Follow the instruction or documentation of FedML to install required package in python environment.

Experimental Tracking Platform

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

Experiment Scripts

Before any experiments, remember to kill any process occupying port 8999. Simply, you can run the following script:
```
lsof -i:8999 # this instruction could show the PID of the process occupying port 8999
kill PID
```
To test whether program are correctly configured, you can run following commands to see whether training process starts correctly.
```
sh begin.sh
```

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

## MNIST (non-i.i.d)
sh ./src/run_fedavg_standalone_pytorch.sh 0 10 mnist /home/zzp1012/FedML/data/mnist lr hetero 50 20 0.03 sgd 0 sch_random -v

## shakespeare (non-i.i.d LEAF)
sh ./src/run_fedavg_standalone_pytorch.sh 0 10 shakespeare /home/zzp1012/FedML/data/shakespeare rnn hetero 50 1 0.8 sgd 0 sch_random -v

# fed_shakespeare (non-i.i.d Google)
sh ./src/run_fedavg_standalone_pytorch.sh 0 10 fed_shakespeare /home/zzp1012/FedML/data/fed_shakespeare rnn hetero 50 1 0.8 sgd 0 sch_random -v

## Federated EMNIST (non-i.i.d)
sh ./src/run_fedavg_standalone_pytorch.sh 0 10 femnist /home/zzp1012/FedML/data/FederatedEMNIST cnn hetero 50 1 0.03 sgd 0 sch_random -v

## Fed_CIFAR100 (non-i.i.d)
sh ./src/run_fedavg_standalone_pytorch.sh 0 10 fed_cifar100 /home/zzp1012/FedML/data/fed_cifar100 resnet18_gn hetero 50 1 0.03 adam 0 sch_random -v

# Stackoverflow_LR (non-i.i.d)
sh ./src/run_fedavg_standalone_pytorch.sh 0 10 stackoverflow_lr /home/zzp1012/FedML/data/stackoverflow lr hetero 50 1 0.03 sgd 0 sch_random -v

# Stackoverflow_NWP (non-i.i.d)
sh ./src/run_fedavg_standalone_pytorch.sh 0 10 stackoverflow_nwp /home/zzp1012/FedML/data/stackoverflow rnn hetero 50 1 0.03 sgd 0 sch_random -v
 
# CIFAR10 (non-i.i.d) 
sh ./src/run_fedavg_standalone_pytorch.sh 0 10 cifar10 /home/zzp1012/FedML/data/cifar10 resnet56 hetero 50 1 0.03 sgd 0 sch_random -v

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

Benchmark Results

We publish benchmark experimental results at wanb.com:
https://app.wandb.ai/automl/fedml