Introduction

A baseline implementation of Federated Learning environment using flower framework. Extending the existing example code provided by flower and modifying it to our needs in order to implement advanced functionalities. Will be extended in our future research.

Pre-requisites

In order to run this code, you need to install either Pytorch and/or Tensorflow with all their pre-requisites. It is recommended to create a fresh virtual environment before carrying out these installations.

Best way to do this is with Conda virtual environment.

conda create -n flower_test
conda activate flower_test # some older version of conda require "source activate flower_test" instead.

The detailed steps regarding installation of PyTorch and Tensorflow can be found at their respective repositories. PyTorch and Tensorflow .

Finally we are ready to install flower framework itself. Simply run

pip install flwr

Running the code

Running the code has two distinct parts i.e. starting up the server and initiating the clients. Each of these steps are explained below.

Staring the Federated Server

First thing we need to do is to run the Federated Server. This can be done by either directly running the server.py file (with appropriate arguments, all possible arguments are discusses in next section) located under the src folder using:

python server.py \
    --server_address=$SERVER_ADDRESS \
    --rounds=25 \
    --sample_fraction=1.0 \
    --min_sample_size=4 \
    --min_num_clients=4

Or we can use the run_server.sh script located under src folder and setup appropriate arguments inside the script before hand.

./run_server.sh

Server Arguments

Argument	Default	Type	Values	Description
server_address	127.0.0.1:8080	str	server_ip:available_port	Use this flag to setup listen interface for server module.
rounds	1	int	(0, +inf)	Use this flage to specify number of rounds of federated training to run.
min_num_clients	2	int	(1, +inf)	Use this flag to tell the server minimum number of clients it should wait for before beginning the training.
min_sample_size	2	int	(0, min_num_clients]	This flag specifies minimum # of clients that should be sampled for any round (training or evaluation).
sample_fraction	1.0	float	(0, 1]	Specifies the fraction of clients to use for fit / evaluate.
model	simple-cnn	str	mlp-mnist, simple-cnn	Model to use for training.
dataset	cifar-10	str	cifar-10, mnist	Dataset to use fro training.
device	CPU	str	CPU, GPU	Device to run the model on.
strategy	FedAvg	str	FedAvg	Aggregation strategy.
epochs	1	int	(1, +inf)	Number of local epochs to run on each client before aggregation.
batch_size	32	int	(1, +inf)	Batch size to be used by each worker.
learning_rate	0.001	float	(0, +inf)	Learning rate to be used by each worker.
quantize	False	bool	True, False	Either to use quantization or not.
quantize_bits	64	int	(2, 64)	Quantization bits.

Starting the Federated Workers

After the server has successfully started next step it to run the Federated Workers. Currently there are two types of workers: the ones that used PyTorch as their DL library and the ones that use Tensorflow as their DL library. We can run either of these clients or a combination of both using the appropriate scripts located under src folder.

Running PyTorch Clients:

./run_clients_pt.sh

Running Tensorflow Clients:

./run_clients_tf.sh

Acknowledgements