To install the required dependencies please use the req.yml file to create your conda environment. Otherwise run the following pip install:
pip install torch datasets transformers flwr["simulation"]
Simulated runs can be done by running the run_simulated.py file. To change the number of clients, number of FL rounds, the model used, training size, etc... you can edit them in the config.py file.
To run across multiple machines simply run client.py on the client machines and run server.py on the server. Make sure to change SERVER_ADDRESS in the config.py file so that your clients can communicate with the server machine.
Non-IID data comes from Tensorflow-Federated (https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets/cifar100). There is a function in flower_helpers.py that can convert it into Pytorch dataloaders. However, we have already generated dataloaders for up to 10 clients which can be downloaded from here: https://drive.google.com/drive/folders/1nQKWMZa2k2w1Sw1CN3WqjFj77Uwj8Rem?usp=share_link.
To train on this data you simply need to change the NON_IID parameter to True and set the address of TFF_DATA_DIR in the config.py file. Note that this is only for CIFAR-100 data so you need to change NUM_CLASSES to 100 to match.