flwr

import flwr as fw import numpy as np from collections import OrderedDict # 1. Define a Flower Client (inheriting from NumPyClient) class CifarClient(fw.client.NumPyClient): def __init__(self): # In a real scenario, you'd load your actual ML model here # For this quickstart, we'll use a dummy model with NumPy arrays. self.model = { "layer1": np.random.rand(10, 10).astype(np.float32), "layer2": np.random.rand(10, 1).astype(np.float32) } def get_parameters(self, config): """Return model parameters as a list of NumPy arrays.""" return [v for v in self.model.values()] def set_parameters(self, parameters): """Set model parameters from a list of NumPy arrays.""" params_dict = zip(self.model.keys(), parameters) self.model = OrderedDict({k: v for k, v in params_dict}) def fit(self, parameters, config): """Simulate training and return updated parameters, number of examples, and metrics.""" self.set_parameters(parameters) # Simulate training: update weights slightly new_weights = {k: v + np.random.rand(*v.shape).astype(np.float32) * 0.1 for k, v in self.model.items()} self.model = OrderedDict(new_weights) num_examples = 100 metrics = {"accuracy": float(np.random.rand())} return self.get_parameters({}), num_examples, metrics def evaluate(self, parameters, config): """Simulate evaluation and return loss, number of examples, and metrics.""" self.set_parameters(parameters) # Simulate evaluation loss = float(np.random.rand() * 0.5 + 0.5) # Loss between 0.5 and 1.0 accuracy = float(np.random.rand() * 0.2 + 0.7) # Accuracy between 0.7 and 0.9 num_examples = 50 return loss, num_examples, {"accuracy": accuracy} # 2. Define a Flower Server Strategy # FedAvg (Federated Averaging) is a common choice. strategy = fw.server.strategy.FedAvg( fraction_fit=1.0, # Sample 100% of available clients for training fraction_evaluate=1.0, # Sample 100% of available clients for evaluation min_fit_clients=2, # Wait for at least 2 clients to participate in fit min_evaluate_clients=2, # Wait for at least 2 clients to participate in evaluate min_available_clients=2, # Total number of clients that need to be connected ) # 3. Define the client factory function for the simulation def client_fn(cid: str): """Returns a Flower Client for a given client ID.""" print(f"Creating client {cid}...") return CifarClient().to_client() # 4. Start the Flower Simulation print("Starting Flower simulation with 2 clients for 3 rounds...") fw.simulation.start_simulation( client_fn=client_fn, num_clients=2, config=fw.server.ServerConfig(num_rounds=3), strategy=strategy, ) print("Simulation finished.")