Flower (FLWR) is a well-liked framework designed to make federated studying experiments accessible and scalable. Under, I present an in depth walkthrough based mostly on the Flower tutorial sequence.
Set up: Want to put in Flower (with different mandatory libraries):
pip set up flwr tensorflow numpy
We are going to want extra libraries like TensorFlow and PyTorch
1. Setting Up the Server
Have to create a file title: server.py.
import flwr as fldef evaluate_fn(server_round: int, parameters, config):
loss = 0.5
accuracy = 0.90
print(f"Spherical {server_round}: loss={loss}, accuracy={accuracy}")
return loss, {"accuracy": accuracy}
technique = fl.server.technique.FedAvg(evaluate_fn=evaluate_fn)
fl.server.start_server(server_address="[::]:8080", technique=technique, config={"num_rounds": 3})
2. Setting Up the Shopper
Now we have to create a file title : consumer.py.
import flwr as fl
import numpy as npdef get_parameters():
return [np.zeros((1,))]
def set_parameters(parameters):
go
def match(parameters, config):
new_parameters = get_parameters()
num_examples = 100
print("Native coaching full: sending replace...")
return new_parameters, num_examples, {}
def consider(parameters, config):
loss = 0.5
num_examples = 100
metrics = {"accuracy": 0.90}
print("Native analysis full")
return loss, num_examples, metrics
consumer = fl.consumer.NumPyClient(get_parameters, match, consider)
fl.consumer.start_numpy_client(server_address="localhost:8080", consumer=consumer)
Beginning the Server
python server.py
Begin the Purchasers
python consumer.py
