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import flwr as fl
import torch
from collections import OrderedDict # For the example provided.
def run_federated_learning():
"""
Sets up and starts a federated learning simulation.
This is a highly conceptual example. Actual implementation requires:
1. A defined model architecture.
2. A training loop using PyTorch or TensorFlow.
3. Data loaders.
4. Proper handling of FL strategies.
"""
return """
Federated Learning Implementation Status
<br><br>
This is a conceptual federated learning implementation. Actual data and the requirements are not implemented.
<br><br>
To implement Federated Learning in reality with all the requirements you need:
<br>1. A defined model architecture: Check the FL Client and model defined with model parameters and model code.
<br>2. A training loop using PyTorch or TensorFlow: Training and validation needs to be provided, also look the parameter setup and the model
<br>3. Data loaders: Data needs to be correctly loaded into the program.
<br>4. Proper handling of FL strategies: FL learning algorithms needs to be correctly provided.
"""
class FlowerClient(fl.client.NumPyClient):
def __init__(self, model, trainloader, valloader):
self.model = model
self.trainloader = trainloader
self.valloader = valloader
def get_parameters(self, config):
return [val.cpu().numpy() for _, val in self.model.state_dict().items()]
def set_parameters(self, parameters):
params_dict = zip(self.model.state_dict().keys(), parameters)
state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
self.model.load_state_dict(state_dict, strict=True)
def fit(self, parameters, config):
self.set_parameters(parameters)
# Train.
print("Train the parameters here.")
return parameters, 1, {}
def evaluate(self, parameters, config):
self.set_parameters(parameters)
# Test (validate).
return 1,1, {"accuracy": 1}
#Flower code
#The parameters needs to be added.
print("Started Simulation FL code") |