def load_data(args, dataset_name):
if dataset_name == "mnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_mnist(args.batch_size)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
args.client_num_in_total = client_num
elif dataset_name == "shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_shakespeare(args.batch_size)
args.client_num_in_total = client_num
else:
if dataset_name == "cifar10":
data_loader = load_partition_data_cifar10
elif dataset_name == "cifar100":
data_loader = load_partition_data_cifar100
elif dataset_name == "cinic10":
data_loader = load_partition_data_cinic10
else:
data_loader = load_partition_data_cifar10
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = data_loader(args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size)
dataset = [train_data_num, test_data_num, train_data_global, test_data_global,
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, class_num]
return dataset
def load_data(args, dataset_name):
if dataset_name == "mnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_mnist(args.batch_size)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
args.client_num_in_total = client_num
elif dataset_name == "femnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_emnist(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_shakespeare(args.batch_size)
args.client_num_in_total = client_num
elif dataset_name == "fed_shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_shakespeare(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "fed_cifar100":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_cifar100(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_lr":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_lr(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_nwp":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_nwp(args.dataset, args.data_dir)
args.client_num_in_total = client_num
else:
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_emnist(args.dataset, args.data_dir)
args.client_num_in_total = client_num
dataset = [train_data_num, test_data_num, train_data_global, test_data_global,
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, class_num]
return dataset
def load_data(args, dataset_name):
# check if the centralized training is enabled
centralized = True if args.client_num_in_total == 1 else False
# check if the full-batch training is enabled
args_batch_size = args.batch_size
if args.batch_size <= 0:
full_batch = True
args.batch_size = 128 # temporary batch size
else:
full_batch = False
if dataset_name == "mnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_mnist(args.batch_size)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
args.client_num_in_total = client_num
elif dataset_name == "femnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_emnist(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_shakespeare(args.batch_size)
args.client_num_in_total = client_num
elif dataset_name == "fed_shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_shakespeare(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "fed_cifar100":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_cifar100(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_lr":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_lr(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_nwp":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_nwp(args.dataset, args.data_dir)
args.client_num_in_total = client_num
else:
if dataset_name == "cifar10":
data_loader = load_partition_data_cifar10
elif dataset_name == "cifar100":
data_loader = load_partition_data_cifar100
elif dataset_name == "cinic10":
data_loader = load_partition_data_cinic10
else:
data_loader = load_partition_data_cifar10
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = data_loader(args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size)
if centralized:
train_data_local_num_dict = {
0: sum(user_train_data_num for user_train_data_num in train_data_local_num_dict.values())}
train_data_local_dict = {
0: [batch for cid in sorted(train_data_local_dict.keys()) for batch in train_data_local_dict[cid]]}
test_data_local_dict = {
0: [batch for cid in sorted(test_data_local_dict.keys()) for batch in test_data_local_dict[cid]]}
args.client_num_in_total = 1
if full_batch:
train_data_global = combine_batches(train_data_global)
test_data_global = combine_batches(test_data_global)
train_data_local_dict = {cid: combine_batches(train_data_local_dict[cid]) for cid in
train_data_local_dict.keys()}
test_data_local_dict = {cid: combine_batches(test_data_local_dict[cid]) for cid in test_data_local_dict.keys()}
args.batch_size = args_batch_size
dataset = [train_data_num, test_data_num, train_data_global, test_data_global,
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, class_num]
return dataset
def load_data(args, dataset_name):
# check if the centralized training is enabled
centralized = True if args.client_num_in_total == 1 else False
# check if the full-batch training is enabled
args_batch_size = args.batch_size
if args.batch_size <= 0:
full_batch = True
args.batch_size = 128 # temporary batch size
else:
full_batch = False
if dataset_name in ["mnist", "fmnist", "emnist"]:
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_mnist(
args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size
)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
args.client_num_in_total = client_num
elif dataset_name == "har":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_ucihar(
args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size
)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
args.client_num_in_total = client_num
elif dataset_name == "har_subject":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_ucihar_subject(
args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size
)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
args.client_num_in_total = client_num
elif dataset_name == "chmnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_chmnist(
args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size
)
args.client_num_in_total = client_num
elif dataset_name == "adult":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_uciadult(
args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size
)
args.client_num_in_total = client_num
elif dataset_name in ["purchase100", "texas100"]:
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_purchase(
args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size
)
args.client_num_in_total = client_num
elif dataset_name == "femnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_emnist(
args.dataset, args.data_dir,
client_num_in_total = args.client_num_in_total,
)
args.client_num_in_total = client_num
elif dataset_name == "shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_shakespeare(args.batch_size)
args.client_num_in_total = client_num
elif dataset_name == "fed_shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_shakespeare(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "fed_cifar100":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_cifar100(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_lr":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_lr(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_nwp":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_nwp(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "ILSVRC2012":
logging.info("load_data. dataset_name = %s" % dataset_name)
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_ImageNet(dataset=dataset_name, data_dir=args.data_dir,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
elif dataset_name == "gld23k":
logging.info("load_data. dataset_name = %s" % dataset_name)
args.client_num_in_total = 233
fed_train_map_file = os.path.join(args.data_dir,
'mini_gld_train_split.csv')
fed_test_map_file = os.path.join(args.data_dir, 'mini_gld_test.csv')
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_landmarks(dataset=dataset_name, data_dir=args.data_dir,
fed_train_map_file=fed_train_map_file,
fed_test_map_file=fed_test_map_file,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
elif dataset_name == "gld160k":
logging.info("load_data. dataset_name = %s" % dataset_name)
args.client_num_in_total = 1262
fed_train_map_file = os.path.join(args.data_dir, 'federated_train.csv')
fed_test_map_file = os.path.join(args.data_dir, 'test.csv')
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_landmarks(dataset=dataset_name, data_dir=args.data_dir,
fed_train_map_file=fed_train_map_file,
fed_test_map_file=fed_test_map_file,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
else:
if dataset_name == "cifar10":
data_loader = load_partition_data_cifar10
elif dataset_name == "cifar100":
data_loader = load_partition_data_cifar100
elif dataset_name == "cinic10":
data_loader = load_partition_data_cinic10
else:
data_loader = load_partition_data_cifar10
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = data_loader(args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size)
if centralized:
train_data_local_num_dict = {
0:
sum(user_train_data_num
for user_train_data_num in train_data_local_num_dict.values())
}
train_data_local_dict = {
0: [
batch for cid in sorted(train_data_local_dict.keys())
for batch in train_data_local_dict[cid]
]
}
test_data_local_dict = {
0: [
batch for cid in sorted(test_data_local_dict.keys())
for batch in test_data_local_dict[cid]
]
}
args.client_num_in_total = 1
if full_batch:
train_data_global = combine_batches(train_data_global)
test_data_global = combine_batches(test_data_global)
train_data_local_dict = {
cid: combine_batches(train_data_local_dict[cid])
for cid in train_data_local_dict.keys()
}
test_data_local_dict = {
cid: combine_batches(test_data_local_dict[cid])
for cid in test_data_local_dict.keys()
}
args.batch_size = args_batch_size
dataset = [
train_data_num, test_data_num, train_data_global, test_data_global,
train_data_local_num_dict, train_data_local_dict, test_data_local_dict,
class_num
]
return dataset
def load_data(args, dataset_name):
if dataset_name == "mnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_mnist(args.batch_size)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
args.client_num_in_total = client_num
elif dataset_name == "femnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_emnist(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_shakespeare(args.batch_size)
args.client_num_in_total = client_num
elif dataset_name == "fed_shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_shakespeare(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "fed_cifar100":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_cifar100(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_lr":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_lr(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_nwp":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_nwp(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "ILSVRC2012":
logging.info("load_data. dataset_name = %s" % dataset_name)
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_ImageNet(dataset=dataset_name, data_dir=args.data_dir,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
elif dataset_name == "gld23k":
logging.info("load_data. dataset_name = %s" % dataset_name)
args.client_num_in_total = 233
fed_train_map_file = os.path.join(args.data_dir,
'mini_gld_train_split.csv')
fed_test_map_file = os.path.join(args.data_dir, 'mini_gld_test.csv')
args.data_dir = os.path.join(args.data_dir, 'images')
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_landmarks(dataset=dataset_name, data_dir=args.data_dir,
fed_train_map_file=fed_train_map_file,
fed_test_map_file=fed_test_map_file,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
elif dataset_name == "gld160k":
logging.info("load_data. dataset_name = %s" % dataset_name)
args.client_num_in_total = 1262
fed_train_map_file = os.path.join(args.data_dir, 'federated_train.csv')
fed_test_map_file = os.path.join(args.data_dir, 'test.csv')
args.data_dir = os.path.join(args.data_dir, 'images')
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_landmarks(dataset=dataset_name, data_dir=args.data_dir,
fed_train_map_file=fed_train_map_file,
fed_test_map_file=fed_test_map_file,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
else:
if dataset_name == "cifar10":
data_loader = load_partition_data_cifar10
elif dataset_name == "cifar100":
data_loader = load_partition_data_cifar100
elif dataset_name == "cinic10":
data_loader = load_partition_data_cinic10
else:
data_loader = load_partition_data_cifar10
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = data_loader(args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size)
dataset = [
train_data_num, test_data_num, train_data_global, test_data_global,
train_data_local_num_dict, train_data_local_dict, test_data_local_dict,
class_num
]
return dataset
def load_data(args, dataset_name):
if dataset_name in ["mnist", "fmnist", "emnist"]:
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_mnist(
args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size,
training_data_ratio=args.training_data_ratio,
)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
args.client_num_in_total = client_num
elif dataset_name == "har":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_ucihar(
args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size
)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
args.client_num_in_total = client_num
elif dataset_name == "chmnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_chmnist(
args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size
)
args.client_num_in_total = client_num
elif dataset_name == "adult":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_uciadult(
args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size
)
args.client_num_in_total = client_num
elif dataset_name in ["purchase100", "texas100"]:
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_purchase(
args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size
)
args.client_num_in_total = client_num
elif dataset_name == "femnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_emnist(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_shakespeare(args.batch_size)
args.client_num_in_total = client_num
elif dataset_name == "fed_shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_shakespeare(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "fed_cifar100":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_cifar100(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_lr":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_lr(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_nwp":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_nwp(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name in ["ILSVRC2012", "ILSVRC2012_hdf5"]:
if args.data_parallel == 1:
logging.info("load_data. dataset_name = %s" % dataset_name)
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = distributed_centralized_ImageNet_loader(dataset=dataset_name, data_dir=args.data_dir,
world_size=args.world_size,
rank=args.rank, batch_size=args.batch_size)
else:
logging.info("load_data. dataset_name = %s" % dataset_name)
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_ImageNet(dataset=dataset_name, data_dir=args.data_dir,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
elif dataset_name == "gld23k":
logging.info("load_data. dataset_name = %s" % dataset_name)
args.client_num_in_total = 233
fed_train_map_file = os.path.join(
args.data_dir, 'data_user_dict/gld23k_user_dict_train.csv')
fed_test_map_file = os.path.join(
args.data_dir, 'data_user_dict/gld23k_user_dict_test.csv')
args.data_dir = os.path.join(args.data_dir, 'images')
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_landmarks(dataset=dataset_name, data_dir=args.data_dir,
fed_train_map_file=fed_train_map_file,
fed_test_map_file=fed_test_map_file,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
elif dataset_name == "gld160k":
logging.info("load_data. dataset_name = %s" % dataset_name)
args.client_num_in_total = 1262
fed_train_map_file = os.path.join(
args.data_dir, 'data_user_dict/gld160k_user_dict_train.csv')
fed_test_map_file = os.path.join(
args.data_dir, 'data_user_dict/gld160k_user_dict_test.csv')
args.data_dir = os.path.join(args.data_dir, 'images')
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_landmarks(dataset=dataset_name, data_dir=args.data_dir,
fed_train_map_file=fed_train_map_file,
fed_test_map_file=fed_test_map_file,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
else:
if dataset_name == "cifar10":
data_loader = load_partition_data_cifar10
elif dataset_name == "cifar100":
data_loader = load_partition_data_cifar100
elif dataset_name == "cinic10":
data_loader = load_partition_data_cinic10
else:
data_loader = load_partition_data_cifar10
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = data_loader(args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size)
dataset = [
train_data_num, test_data_num, train_data_global, test_data_global,
train_data_local_num_dict, train_data_local_dict, test_data_local_dict,
class_num
]
return dataset
def load_data(args, dataset_name):
# check if the centralized training is enabled（如果总共只有一个设备参与，则认为是中心化训练）
centralized = True if args.client_num_in_total == 1 else False
# check if the full-batch training is enabled
args_batch_size = args.batch_size
if args.batch_size <= 0: # 为什么是<=0？
full_batch = True
args.batch_size = 128 # temporary batch size
else:
full_batch = False
# assign a initial
feature_name = None
if dataset_name == "mnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_mnist(args.batch_size)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
args.client_num_in_total = client_num
elif dataset_name == "mnist_test":
logging.info("load_data. dataset_name = %s" % dataset_name)
# 使用自带的dataloader方法进行划分
# client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
# train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
# class_num = load_partition_data_mnist(args.batch_size)
# args.client_num_in_total = client_num
# 更具有灵活性的指定，进行数据划分
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_mnist_asign(args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size)
elif dataset_name == "cervical_cancer":
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num, feature_name = load_partition_data_cervical(args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size)
elif dataset_name == "femnist":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_emnist(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_shakespeare(args.batch_size)
args.client_num_in_total = client_num
elif dataset_name == "fed_shakespeare":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_shakespeare(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "fed_cifar100":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_cifar100(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_lr":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_lr(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "stackoverflow_nwp":
logging.info("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_nwp(args.dataset, args.data_dir)
args.client_num_in_total = client_num
elif dataset_name == "ILSVRC2012":
logging.info("load_data. dataset_name = %s" % dataset_name)
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_ImageNet(dataset=dataset_name, data_dir=args.data_dir,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
elif dataset_name == "gld23k":
logging.info("load_data. dataset_name = %s" % dataset_name)
args.client_num_in_total = 233
fed_train_map_file = os.path.join(args.data_dir,
'mini_gld_train_split.csv')
fed_test_map_file = os.path.join(args.data_dir, 'mini_gld_test.csv')
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_landmarks(dataset=dataset_name, data_dir=args.data_dir,
fed_train_map_file=fed_train_map_file,
fed_test_map_file=fed_test_map_file,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
elif dataset_name == "gld160k":
logging.info("load_data. dataset_name = %s" % dataset_name)
args.client_num_in_total = 1262
fed_train_map_file = os.path.join(args.data_dir, 'federated_train.csv')
fed_test_map_file = os.path.join(args.data_dir, 'test.csv')
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_landmarks(dataset=dataset_name, data_dir=args.data_dir,
fed_train_map_file=fed_train_map_file,
fed_test_map_file=fed_test_map_file,
partition_method=None, partition_alpha=None,
client_number=args.client_num_in_total, batch_size=args.batch_size)
else:
if dataset_name == "cifar10":
data_loader = load_partition_data_cifar10
elif dataset_name == "cifar100":
data_loader = load_partition_data_cifar100
elif dataset_name == "cinic10":
data_loader = load_partition_data_cinic10
else:
data_loader = load_partition_data_cifar10
train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = data_loader(args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, args.client_num_in_total, args.batch_size)
# 如果是中心化训练(只有一个设备参与)，修改客户端本地数据映射字典，全部映射到0号
if centralized:
train_data_local_num_dict = {
0:
sum(user_train_data_num
for user_train_data_num in train_data_local_num_dict.values())
}
train_data_local_dict = {
0: [
batch for cid in sorted(train_data_local_dict.keys())
for batch in train_data_local_dict[cid]
]
}
test_data_local_dict = {
0: [
batch for cid in sorted(test_data_local_dict.keys())
for batch in test_data_local_dict[cid]
]
}
args.client_num_in_total = 1
# 如果是全批次训练，则
if full_batch:
train_data_global = combine_batches(train_data_global)
test_data_global = combine_batches(test_data_global)
train_data_local_dict = {
cid: combine_batches(train_data_local_dict[cid])
for cid in train_data_local_dict.keys()
}
test_data_local_dict = {
cid: combine_batches(test_data_local_dict[cid])
for cid in test_data_local_dict.keys()
}
args.batch_size = args_batch_size
dataset = [
train_data_num, test_data_num, train_data_global, test_data_global,
train_data_local_num_dict, train_data_local_dict, test_data_local_dict,
class_num, feature_name
]
return dataset
def load_data(args, dataset_name):
# check if the full-batch training is enabled
args_batch_size = args.batch_size
if args.batch_size <= 0:
full_batch = True
args.batch_size = 128 # temporary batch size
else:
full_batch = False
logger.info("-------------dataset loading------------")
if dataset_name == "mnist":
logger.debug("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_mnist(args.batch_size)
"""
For shallow NN or linear models,
we uniformly sample a fraction of clients each round (as the original FedAvg paper)
"""
elif dataset_name == "femnist":
logger.debug("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_emnist(args.dataset, args.data_dir)
elif dataset_name == "shakespeare":
logger.debug("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_shakespeare(args.batch_size)
elif dataset_name == "fed_shakespeare":
logger.debug("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_shakespeare(args.dataset, args.data_dir)
elif dataset_name == "fed_cifar100":
logger.debug("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_cifar100(args.dataset, args.data_dir)
elif dataset_name == "stackoverflow_lr":
logger.debug("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_lr(args.dataset, args.data_dir)
elif dataset_name == "stackoverflow_nwp":
logger.debug("load_data. dataset_name = %s" % dataset_name)
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = load_partition_data_federated_stackoverflow_nwp(args.dataset, args.data_dir)
else:
if dataset_name == "cifar10":
data_loader = load_partition_data_cifar10
elif dataset_name == "cifar100":
data_loader = load_partition_data_cifar100
elif dataset_name == "cinic10":
data_loader = load_partition_data_cinic10
else:
data_loader = load_partition_data_cifar10
client_num, train_data_num, test_data_num, train_data_global, test_data_global, \
train_data_local_num_dict, train_data_local_dict, test_data_local_dict, \
class_num = data_loader(args.dataset, args.data_dir, args.partition_method,
args.partition_alpha, client_num_in_total, args.batch_size)
if full_batch:
train_data_global = combine_batches(train_data_global)
test_data_global = combine_batches(test_data_global)
train_data_local_dict = {
cid: combine_batches(train_data_local_dict[cid])
for cid in train_data_local_dict.keys()
}
test_data_local_dict = {
cid: combine_batches(test_data_local_dict[cid])
for cid in test_data_local_dict.keys()
}
args.batch_size = args_batch_size
dataset = [
client_num, train_data_num, test_data_num, train_data_global,
test_data_global, train_data_local_num_dict, train_data_local_dict,
test_data_local_dict, class_num
]
return dataset
