components/federated_learning.py

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.
    """

    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")