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FEMNIST_Imbalanced_skd.py
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FEMNIST_Imbalanced_skd.py
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import os
import errno
import argparse
import sys
import pickle
import numpy as np
from tensorflow.keras.models import load_model
from data_utils import load_MNIST_data, load_EMNIST_data, generate_EMNIST_writer_based_data, generate_partial_data
from skd import FedMD
from Neural_Networks import train_models, cnn_2layer_fc_model, cnn_3layer_fc_model
import tensorflow as tf
import random as rn
os.environ['PYTHONHASHSEED'] = '0'
np.random.seed(17)
rn.seed(42)
tf.random.set_seed(29)
def parseArg():
parser = argparse.ArgumentParser(description='FedMD, a federated learning framework. \
Participants are training collaboratively. ')
parser.add_argument('-conf', metavar='conf_file', nargs=1,
help='the config file for FedMD.'
)
conf_file = os.path.abspath("conf/EMNIST_imbalance_conf.json")
if len(sys.argv) > 1:
args = parser.parse_args(sys.argv[1:])
if args.conf:
conf_file = args.conf[0]
return conf_file
# label poisoning
def poisoning(targets):
for idx in range(len(targets)):
if targets[idx] == 10:
targets[idx] = 14
elif targets[idx] == 11:
targets[idx] = 12
elif targets[idx] == 12:
targets[idx] = 11
elif targets[idx] == 13:
targets[idx] = 15
elif targets[idx] == 14:
targets[idx] = 10
elif targets[idx] == 15:
targets[idx] = 13
return targets
CANDIDATE_MODELS = {"2_layer_CNN": cnn_2layer_fc_model,
"3_layer_CNN": cnn_3layer_fc_model}
if __name__ == "__main__":
conf_file = parseArg()
with open(conf_file, "r") as f:
conf_dict = eval(f.read())
#n_classes = conf_dict["n_classes"]
model_config = conf_dict["models"]
pre_train_params = conf_dict["pre_train_params"]
model_saved_dir = conf_dict["model_saved_dir"]
model_saved_names = conf_dict["model_saved_names"]
is_early_stopping = conf_dict["early_stopping"]
public_classes = conf_dict["public_classes"]
private_classes = conf_dict["private_classes"]
n_classes = len(public_classes) + len(private_classes)
emnist_data_dir = conf_dict["EMNIST_dir"]
N_parties = conf_dict["N_parties"]
N_samples_per_class = conf_dict["N_samples_per_class"]
N_rounds = conf_dict["N_rounds"]
N_alignment = conf_dict["N_alignment"]
N_private_training_round = conf_dict["N_private_training_round"]
private_training_batchsize = conf_dict["private_training_batchsize"]
N_logits_matching_round = conf_dict["N_logits_matching_round"]
logits_matching_batchsize = conf_dict["logits_matching_batchsize"]
result_save_dir = conf_dict["result_save_dir"]
del conf_dict, conf_file
X_train_MNIST, y_train_MNIST, X_test_MNIST, y_test_MNIST \
= load_MNIST_data(standarized = True, verbose = True)
public_dataset = {"X": X_train_MNIST, "y": y_train_MNIST}
X_train_EMNIST, y_train_EMNIST, X_test_EMNIST, y_test_EMNIST, \
writer_ids_train_EMNIST, writer_ids_test_EMNIST \
= load_EMNIST_data(emnist_data_dir,
standarized = True, verbose = True)
y_train_EMNIST += len(public_classes)
y_test_EMNIST += len(public_classes)
#generate private data
private_data, total_private_data\
=generate_EMNIST_writer_based_data(X_train_EMNIST, y_train_EMNIST,
writer_ids_train_EMNIST,
N_parties = N_parties,
classes_in_use = private_classes,
N_priv_data_min = N_samples_per_class * len(private_classes)
)
X_tmp, y_tmp = generate_partial_data(X = X_test_EMNIST, y= y_test_EMNIST,
class_in_use = private_classes, verbose = True)
private_test_data = {"X": X_tmp, "y": y_tmp}
del X_tmp, y_tmp
# Poisoning
poison_devices = [0,1,2,3]
for device in poison_devices:
private_data[device]['y'] = poisoning(private_data[device]['y'])
print(private_data[device]['y'])
parties = []
if model_saved_dir is None:
for i, item in enumerate(model_config):
model_name = item["model_type"]
model_params = item["params"]
tmp = CANDIDATE_MODELS[model_name](n_classes=n_classes,
input_shape=(28,28),
**model_params)
print("model {0} : {1}".format(i, model_saved_names[i]))
print(tmp.summary())
parties.append(tmp)
del model_name, model_params, tmp
#END FOR LOOP
pre_train_result = train_models(parties,
X_train_MNIST, y_train_MNIST,
X_test_MNIST, y_test_MNIST,
save_dir = model_saved_dir, save_names = model_saved_names,
early_stopping = is_early_stopping,
**pre_train_params
)
else:
dpath = os.path.abspath(model_saved_dir)
model_names = os.listdir(dpath)
for name in model_names:
tmp = None
tmp = load_model(os.path.join(dpath ,name))
parties.append(tmp)
del X_train_MNIST, y_train_MNIST, X_test_MNIST, y_test_MNIST, \
X_train_EMNIST, y_train_EMNIST, X_test_EMNIST, y_test_EMNIST, writer_ids_train_EMNIST, writer_ids_test_EMNIST
fedmd = FedMD(parties,
public_dataset = public_dataset,
private_data = private_data,
total_private_data = total_private_data,
private_test_data = private_test_data,
N_rounds = N_rounds,
N_alignment = N_alignment,
N_logits_matching_round = N_logits_matching_round,
logits_matching_batchsize = logits_matching_batchsize,
N_private_training_round = N_private_training_round,
private_training_batchsize = private_training_batchsize)
initialization_result = fedmd.init_result
pooled_train_result = fedmd.pooled_train_result
collaboration_performance = fedmd.collaborative_training()
if result_save_dir is not None:
save_dir_path = os.path.abspath(result_save_dir)
#make dir
try:
os.makedirs(save_dir_path)
except OSError as e:
if e.errno != errno.EEXIST:
raise
with open(os.path.join(save_dir_path, 'pre_train_result.pkl'), 'wb') as f:
pickle.dump(pre_train_result, f, protocol=pickle.HIGHEST_PROTOCOL)
with open(os.path.join(save_dir_path, 'init_result.pkl'), 'wb') as f:
pickle.dump(initialization_result, f, protocol=pickle.HIGHEST_PROTOCOL)
with open(os.path.join(save_dir_path, 'pooled_train_result.pkl'), 'wb') as f:
pickle.dump(pooled_train_result, f, protocol=pickle.HIGHEST_PROTOCOL)
with open(os.path.join(save_dir_path, 'col_performance.pkl'), 'wb') as f:
pickle.dump(collaboration_performance, f, protocol=pickle.HIGHEST_PROTOCOL)