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client.py
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client.py
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import argparse
import json
import threading
import time
from random import random
import numpy as np
import requests
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Quiet tensorflow error messages
class Client:
def __init__(self, ip_address, max_round, time_delay = 5, num_samples=600, client_id = 0, experiment = 1):
# URLs
self.session = requests.Session()
self.base_url = ip_address
self.update_num_data_url = self.base_url + "/update_num_data/" + str(client_id)
self.put_weight_url = self.base_url + "/put_local_weight/" + str(client_id)
self.get_weight_url = self.base_url + "/get_server_weight"
self.round_url = self.base_url + "/get_server_round"
self.get_model_url = self.base_url + "/get_server_model"
self.get_compile_config_url = self.base_url + "/get_compile_config"
# setting up variables
self.experiment = experiment
self.client_id = client_id
self.time_delay = time_delay
self.global_round = self.request_global_round()
self.current_round = 0
self.max_round = max_round # Set the maximum number of rounds
# Download mnist dataset
self.train_images, self.train_labels, self.test_images, self.test_labels = self.prepare_images()
# Splits dataset (iid, random, )
self.split_train_images, self.split_train_labels = self.data_split(num_samples)
self.local_data_num = len(self.split_train_labels)
# Brings model architecture, optimizer, loss, metrics from the server
self.model = self.build_model_from_server()
def prepare_images(self):
mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images, test_images = train_images / 255, test_images / 255
# For CNN, add dummy channel to feed the images to CNN
train_images=train_images.reshape(-1,28, 28, 1)
test_images=test_images.reshape(-1,28, 28, 1)
return train_images, train_labels, test_images, test_labels
def build_model_from_server(self):
model = self.session.get(self.get_model_url).json()
# None converted in null, which is unexpected..
# Remap null to None
model = tf.keras.models.model_from_json(model, custom_objects={"null":None})
optimizer, loss, metrics = self.request_compile_config()
model.compile(optimizer=optimizer,loss=loss,metrics=metrics)
return model
def data_split(self, num_samples):
# Take a batch of num_samples according to self.experiment
# 1 : iid
# 2 : Randomly selected, equally sized dataset
# 3 : Randomly selected, differently sized dataset
# 4 : Skewed dataset
train_index_list = [[], [], [], [], [], [], [], [], [], []]
test_index_list = [[], [], [], [], [], [], [], [], [], []]
for i, v in enumerate(self.train_labels):
train_index_list[v].append(i)
for i, v in enumerate(self.test_labels):
test_index_list[v].append(i)
split_train_images = []
split_train_labels = []
if self.experiment == 1: #uniform data split
# all
self.local_data_num = num_samples
for i in range(len(train_index_list)):
indices = train_index_list[i]
random_indices = np.random.choice(indices, size=num_samples//10)
split_train_images.extend(self.train_images[random_indices])
split_train_labels.extend(self.train_labels[random_indices])
elif self.experiment == 2: # Randomly selected, equally sized dataset
self.local_data_num = num_samples
random_indices = np.random.choice([i for i in range(len(self.train_labels))], size=num_samples)
split_train_images = self.train_images[random_indices]
split_train_labels = self.train_labels[random_indices]
elif self.experiment == 3: # Randomly selected, differently sized dataset
n = np.random.randint(1, num_samples)
self.local_data_num = n
random_indices = np.random.choice([i for i in range(len(self.train_labels))], size=n)
split_train_images = self.train_images[random_indices]
split_train_labels = self.train_labels[random_indices]
elif self.experiment == 4: #Skewed
all_labels = [i for i in range(10)]
skewed_numbers = np.random.choice(all_labels, np.random.randint(1, 10))
non_skewed_numbers = set(all_labels)-set(skewed_numbers)
N = 0
for i in skewed_numbers:
n = np.random.randint(50, 60)
N += n
indices = train_index_list[i]
random_indices = np.random.choice(indices, size=n)
split_train_images.extend(self.train_images[random_indices])
split_train_labels.extend(self.train_labels[random_indices])
for i in non_skewed_numbers:
n = np.random.randint(1, 10)
N += n
indices = train_index_list[i]
random_indices = np.random.choice(indices, size=n)
split_train_images.extend(self.train_images[random_indices])
split_train_labels.extend(self.train_labels[random_indices])
self.local_data_num = N
# convert split dataset into numpy array
split_train_images = np.array(split_train_images)
split_train_labels = np.array(split_train_labels)
return split_train_images, split_train_labels
def update_total_num_data(self, num_data):
# Upload client's num_data to the server
self.session.get(self.update_num_data_url +"/" + str(num_data))
def request_compile_config(self):
# Request optimizer, loss, metrics defined in the server
compile_config = self.session.get(self.get_compile_config_url).json()
optim = tf.keras.optimizers.deserialize(compile_config["optim"])
loss = tf.keras.losses.deserialize(compile_config["loss"])
metrics = compile_config["metrics"]
return optim, loss, metrics
def request_global_round(self):
# Request the global round
result = self.session.get(self.round_url)
result = result.json()
return result
def request_global_weight(self):
# Request the global model (parameters)
result = self.session.get(self.get_weight_url)
result_data = result.json()
global_weight = None
if result_data is not None:
global_weight = []
for i in range(len(result_data)):
temp = np.array(result_data[i], dtype=np.float32)
global_weight.append(temp)
return global_weight
def upload_local_weight(self, local_weight):
# Upload trained local model parameters
for i in range(len(local_weight)):
local_weight[i] = local_weight[i].tolist()
local_weight_to_json = json.dumps(local_weight)
self.session.put(self.put_weight_url, data=local_weight_to_json)
def train_local_model(self):
# Train a local model from latest model parameters
print("train started")
global_weight = self.request_global_weight()
if global_weight != None:
global_weight = list(map(lambda weight: np.array(weight), global_weight))
self.model.set_weights(global_weight)
self.model.fit(self.split_train_images, self.split_train_labels, epochs=10, batch_size=8, verbose=0)
local_weight = self.model.get_weights()
return local_weight
def task(self):
"""
Federated learning task
1. If the current round is larger than the max round that we set, finish
2. If the global round = current client's round, the client needs update
3. Otherwise, we need to wait until other clients to finish
"""
self.global_round = self.request_global_round()
print(f"Client {self.client_id} current round {self.current_round} global round {self.global_round}")
if self.current_round >= self.max_round: #need to terminate
print(f"Client {self.client_id} finished")
return
if self.global_round == self.current_round: #need to update
print(f"Client {str(self.client_id)} needs update")
self.split_train_images, self.split_train_labels = self.data_split(num_samples=self.local_data_num)
self.update_total_num_data(self.local_data_num)
self.current_round += 1
local_weight = self.train_local_model()
self.upload_local_weight(local_weight)
time.sleep(self.time_delay)
return self.task()
else: #need to wait until other clients finish
print(f"Client {self.client_id} needs wait")
time.sleep(self.time_delay)
return self.task()
"""if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ip", type=str, help="ip address of the server", default="http://147.47.200.178:9103/")
parser.add_argument("--round", '-r', type=int, help="max round", default=5)
parser.add_argument("--num", '-n', type=int, help="number of samples (ignored if exp == 3, 4)", default=600)
parser.add_argument("--id", type=int, help="client id", default=0)
parser.add_argument("--exp", type=int, help="experiment number", default=1)
parser.add_argument("--delay", type=int, help="time delay in seconds", default=5)
args = parser.parse_args()
client = Client(args.ip, args.round, args.delay, args.num, args.id, args.exp)
client.task()"""