def add_client(self, c: Client):
if c.id not in self.clients.keys():
if not self.is_full():
self.clients[c.id] = c
self.client_ids.append(c.id)
c.set_group(self)
else:
print("Warning: Group {:2d} is full.".format(self.id))
else:
print("Warning: Client {} alreay in {:2d} group.".format(c.id, self.id))
def setup_clients(self, dataset, data_partition_seed, dataset_adv, model=None):
'''instantiates clients based on given train and test data directories
Return:
list of Clients
'''
users, groups, train_data, test_data = dataset
# AM:
users_adv, groups_adv, train_data_adv, test_data_adv = dataset_adv
if len(groups) == 0:
groups = [None for _ in users]
if len(groups_adv) == 0:
groups_adv = [None for _ in users_adv]
all_clients = [Client(u, g, train_data[u], test_data[u], data_partition_seed, model) for u, g in zip(users, groups)]
# AM:
all_clients_adv = [Client(u, g, train_data_adv[u], test_data_adv[u], data_partition_seed, model, is_adversary=True) for u, g in zip(users_adv, groups_adv)]
return (all_clients + all_clients_adv)
def setup_clients(self, dataset, model=None, model1=None):
'''instantiates clients based on given train and test data directories
Return:
list of Clients
'''
users, groups, train_data, test_data = dataset
if len(groups) == 0:
groups = [None for _ in users]
client_low = []
client_high = []
# print(len(users))
i = 0
for u, g in zip(users, groups):
if i < self.original_params['drop_percent'] * len(users):
k = 0
if "synthetic" in self.original_params['dataset']:
self.original_params['num_features'] = 60
k = random.randint(
k, self.original_params['num_features'] / 2 - 1)
for j in range(0, len(train_data[u]['x'])):
train_data[u]['x'][j] = train_data[u]['x'][
j][k:k +
int(self.original_params['num_features'] / 2)]
for j in range(0, len(test_data[u]['x'])):
test_data[u]['x'][j] = test_data[u]['x'][
j][k:k +
int(self.original_params['num_features'] / 2)]
startarray = [
k, k + 1, k + 2, k + 3, k + 4, k + 5, k + 6, k + 7,
k + 8, k + 9, k + 10, k + 11, k + 12, k + 13, k + 14,
k + 15, k + 16, k + 17, k + 18, k + 19, k + 20, k + 21,
k + 22, k + 23, k + 24, k + 25, k + 26, k + 27, k + 28,
k + 29
]
client_low.append(
Client(u, g, train_data[u], test_data[u], model,
startarray))
else:
client_high.append(
Client(u, g, train_data[u], test_data[u], model1))
i = i + 1
return client_low, client_high
def setup_clients(self, dataset, model=None):
'''instantiates clients based on given train and test data directories
Return:
list of Clients
'''
users, groups, train_data, test_data = dataset
if len(groups) == 0:
groups = [None for _ in users]
all_clients = [Client(u, g, train_data[u], test_data[u], model) for u, g in zip(users, groups)]
return all_clients
def setup_clients(self, dataset, dataset_name, model_name, model=None):
'''instantiates clients based on given train and test data directories
Return:
list of Clients
'''
users, groups, train_data, test_data = dataset
if len(groups) == 0:
groups = [None for _ in users]
all_clients = [Client(id=u, group=g, dataset_name=dataset_name, model_name=model_name, # noqa: E501
train_data=train_data[u], eval_data=test_data[u], model=model) for u, g in zip(users, groups)] # noqa: E501
return all_clients
def setup_clients(self, dataset, model=None):
'''instantiates clients based on given train and test data directories
Return:
list of Clients
'''
users, groups, train_data, test_data = dataset
# If groups is empty, then it does something with nulling for every underscore in users
if len(groups) == 0:
groups = [None for _ in users]
# The data in users and groups is being indexed together with zip()
all_clients = [
Client(u, g, train_data[u], test_data[u], model)
for u, g in zip(users, groups)
]
return all_clients
def setup_clients(self, dataset, model=None):
"""
设置客户端
:param dataset: 数据集元素
:param model: 模型
:return:
"""
users, groups, train_data, test_data = dataset
assert 'x' in train_data[users[-1]], '只能支持处理后的数据'
if len(groups) == 0:
groups = [None for _ in users]
all_clients = [
Client(u, g, train_data[u], test_data[u], model)
for u, g in zip(users, groups)
]
return all_clients
