add Flower_v1/client_2.py -- GPU check and add randomseed (deterministic/reprodu)

Flower_v1/client_2.py

+# python3 client_1.py <dataset> <partition> <client_num> <IP:PORT>
+# python3 client_1.py cifar10 1 3 172.16.66.55:8080
+# this code is updated version with randomseed added to make sure the reproducibility of exp + check the GPU available (to make sure)
+
+import flwr as fl
+import tensorflow as tf
+from sklearn.model_selection import train_test_split
+import numpy as np
+import sys
+import random
+
+physical_devices = tf.config.list_physical_devices('GPU')
+if physical_devices:
+    tf.config.experimental.set_memory_growth(physical_devices[0], True)
+    print("GPU detected and memory growth enabled.")
+else:
+    print("No GPU detected, using CPU.")
+    
+# Set random seed for reproducibility
+def set_random_seed(seed):
+    random.seed(seed)  # Python random module seed
+    np.random.seed(seed)  # Numpy random seed
+    tf.random.set_seed(seed)  # TensorFlow random seed
+
+# Set a specific seed value
+set_random_seed(42)
+
+
+# Function to load and partition the dataset based on client_id
+def load_partitioned_data(dataset_name, num_clients, client_id):
+    if dataset_name == "cifar10":
+        '''
+        total samples train: 50000
+        total samples test: 10000
+        sample type: animals, vehicles, etc. 
+        '''
+        (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
+        x_train = x_train.astype("float32") / 255.0
+        x_test = x_test.astype("float32") / 255.0
+        num_classes = 10
+        input_shape = (32, 32, 3)
+    elif dataset_name == "mnist":
+        '''
+        total samples train: 60000
+        total samples test: 10000
+        sample type: handwritten digits
+        '''
+        (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
+        x_train = x_train.reshape(-1, 28, 28, 1).astype("float32") / 255.0
+        x_test = x_test.reshape(-1, 28, 28, 1).astype("float32") / 255.0
+        num_classes = 10
+        input_shape = (28, 28, 1)
+    elif dataset_name == "cifar100":
+        '''
+        total samples train: 50000
+        total samples test: 10000
+        sample type: animals, objects
+        '''
+        (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar100.load_data()
+        x_train = x_train.astype("float32") / 255.0
+        x_test = x_test.astype("float32") / 255.0
+        num_classes = 100
+        input_shape = (32, 32, 3)
+    else:
+        raise ValueError("Dataset not supported. Use 'cifar10', 'mnist', or 'cifar100'.")
+    
+    # Partition the dataset among the clients (same data distribution with same number_clients setup)
+    total_samples = x_train.shape[0]
+    samples_per_client = total_samples // num_clients
+
+    start = client_id * samples_per_client
+    end = (client_id + 1) * samples_per_client if client_id != num_clients - 1 else total_samples
+
+    x_client_train = x_train[start:end]
+    y_client_train = y_train[start:end]
+    
+    # Split the client-specific data into training and validation sets
+    x_train, x_val, y_train, y_val = train_test_split(x_client_train, y_client_train, test_size=0.2, random_state=42)
+
+    return (x_train, y_train), (x_val, y_val), (x_test, y_test), num_classes, input_shape
+
+# Get command-line arguments: dataset, client_id, num_clients, and server address
+if len(sys.argv) != 5:
+    print("Usage: python3 client_1.py <dataset> <partition> <client_num> <IP:PORT>")
+    sys.exit(1)
+
+dataset_name = sys.argv[1]
+client_id = int(sys.argv[2])
+num_clients = int(sys.argv[3])
+server_address = sys.argv[4]
+
+# Load partitioned data for the client
+(x_train, y_train), (x_val, y_val), (x_test, y_test), num_classes, input_shape = load_partitioned_data(dataset_name, num_clients, client_id)
+
+# Define the model (MobileNetV2)
+model = tf.keras.applications.MobileNetV2(input_shape=input_shape, classes=num_classes, weights=None)
+model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
+
+# Define Flower client
+class FederatedClient(fl.client.NumPyClient):
+    def get_parameters(self, config):
+        return model.get_weights()
+
+    def fit(self, parameters, config):
+        model.set_weights(parameters)
+        model.fit(x_train, y_train, epochs=1, batch_size=32, validation_data=(x_val, y_val))
+        return model.get_weights(), len(x_train), {}
+
+    def evaluate(self, parameters, config):
+        model.set_weights(parameters)
+        loss, accuracy = model.evaluate(x_test, y_test, verbose=0)
+        return loss, len(x_test), {"accuracy": float(accuracy)}
+
+# Start Flower client
+if __name__ == "__main__":
+    fl.client.start_client(server_address=server_address, 
+                           client=FederatedClient(),
+                           max_retries=3, # retry connection to server 3 times
+                           max_wait_time=5) # wait 5 seconds before retrying