def LoanTrain(helper, start_epoch, local_model, target_model, is_poison,
state_keys):
epochs_submit_upd_dict = dict()
num_samples_dict = dict()
curr_adversary_count = len(helper.params['adversary_list'])
for model_id in range(helper.params['no_models']):
epochs_local_upd_list = []
last_params_vars = dict()
client_grad = [] # fg only works for aggr_epoch_interval=1
for name, param in target_model.named_parameters():
last_params_vars[name] = target_model.state_dict()[name].clone()
state_key = state_keys[model_id]
# Synchronize LR and models
model = local_model
model.copy_params(target_model.state_dict())
optimizer = optim.SGD(model.parameters(),
lr=helper.params['lr'],
momentum=helper.params['momentum'],
weight_decay=helper.params['decay'])
model.train()
temp_local_epoch = start_epoch - 1
adversarial_index = -1
localmodel_poison_epochs = helper.params['poison_epochs']
if is_poison and state_key in helper.params['adversary_list']:
for adver_idx in range(0, len(helper.params['adversary_list'])):
if state_key == helper.params['adversary_list'][adver_idx]:
localmodel_poison_epochs = helper.params[str(adver_idx) +
'_poison_epochs']
adversarial_index = adver_idx
main.logger.info(
f'poison local model {state_key} will poison epochs: {localmodel_poison_epochs}'
)
break
if len(helper.params['adversary_list']) == 1:
adversarial_index = -1 # attack the global trigger
trigger_names = []
trigger_values = []
if adversarial_index == -1:
for j in range(0, helper.params['trigger_num']):
for name in helper.params[str(j) + '_poison_trigger_names']:
trigger_names.append(name)
for value in helper.params[str(j) + '_poison_trigger_values']:
trigger_values.append(value)
else:
trigger_names = helper.params[str(adversarial_index) +
'_poison_trigger_names']
trigger_values = helper.params[str(adversarial_index) +
'_poison_trigger_values']
for epoch in range(start_epoch,
start_epoch + helper.params['aggr_epoch_interval']):
# This is for calculating distances
target_params_vars = dict()
for name, param in target_model.named_parameters():
# target_params_vars[name] = last_params_vars[name].clone().detach().requires_grad_(False)
target_params_vars[name] = last_params_vars[name].detach(
).clone()
if is_poison and state_key in helper.params['adversary_list'] and (
epoch in localmodel_poison_epochs):
main.logger.info('poison_now')
_, acc_p, _, _ = test.Mytest_poison(helper,
epoch,
model,
is_poison=True,
visualize=False,
agent_name_key=state_key)
main.logger.info(acc_p)
poison_lr = helper.params['poison_lr']
if not helper.params['baseline']:
if acc_p > 20:
poison_lr /= 5
if acc_p > 60:
poison_lr /= 10
internal_epoch_num = helper.params['internal_poison_epochs']
step_lr = helper.params['poison_step_lr']
poison_optim = optim.SGD(model.parameters(),
lr=poison_lr,
momentum=helper.params['momentum'],
weight_decay=helper.params['decay'])
scheduler = optim.lr_scheduler.MultiStepLR(
poison_optim,
gamma=0.1,
milestones=[
0.2 * internal_epoch_num, 0.8 * internal_epoch_num
])
# acc = acc_initial
for internal_epoch in range(1, internal_epoch_num + 1):
temp_local_epoch += 1
poison_data = helper.statehelper_dic[
state_key].get_poison_trainloader()
if step_lr:
scheduler.step()
main.logger.info(f'Current lr: {scheduler.get_lr()}')
data_iterator = poison_data
poison_data_count = 0
total_loss = 0.
correct = 0
data_size = 0
for batch_id, batch in enumerate(data_iterator):
for index in range(
0, helper.params['poisoning_per_batch']):
if index >= len(batch[1]):
break
batch[1][index] = helper.params[
'poison_label_swap']
for j in range(0, len(trigger_names)):
name = trigger_names[j]
value = trigger_values[j]
batch[0][index][
helper.feature_dict[name]] = value
poison_data_count += 1
data, targets = helper.statehelper_dic[
state_key].get_batch(poison_data, batch, False)
poison_optim.zero_grad()
data_size += len(data)
output = model(data)
class_loss = F.cross_entropy(output, targets)
distance_loss = helper.model_dist_norm_var(
model, target_params_vars)
loss = helper.params['alpha_loss'] * class_loss + (
1 - helper.params['alpha_loss']) * distance_loss
loss.backward()
# get gradients
if helper.params[
'aggregation_methods'] == config.AGGR_FOOLSGOLD:
for i, (name, params) in enumerate(
model.named_parameters()):
if params.requires_grad:
if internal_epoch == 1 and batch_id == 0:
client_grad.append(params.grad.clone())
else:
client_grad[i] += params.grad.clone()
poison_optim.step()
total_loss += loss.data
pred = output.data.max(1)[
1] # get the index of the max log-probability
correct += pred.eq(
targets.data.view_as(pred)).cpu().sum().item()
acc = 100.0 * (float(correct) / float(data_size))
total_l = total_loss / data_size
main.logger.info(
'_PoisonTrain {}, epoch {:3d}, local model {}, internal_epoch {:3d}, Avg loss: {:.4f},'
'Accuracy: {}/{} ({:.4f}%), train_poison_data_count{}'.
format(model.name, epoch, state_key, internal_epoch,
total_l, correct, data_size, acc,
poison_data_count))
csv_record.train_result.append([
state_key, temp_local_epoch, epoch, internal_epoch,
total_l.item(), acc, correct, data_size
])
if helper.params['vis_train']:
model.train_vis(main.vis,
temp_local_epoch,
acc,
loss=total_l,
eid=helper.params['environment_name'],
is_poisoned=True,
name=state_key)
num_samples_dict[state_key] = data_size
# internal epoch finish
main.logger.info(
f'Global model norm: {helper.model_global_norm(target_model)}.'
)
main.logger.info(
f'Norm before scaling: {helper.model_global_norm(model)}. '
f'Distance: {helper.model_dist_norm(model, target_params_vars)}'
)
# Adversary wants to scale his weights. Baseline model doesn't do this
# Some NORM work happening here
if not helper.params['baseline']:
clip_rate = helper.params['scale_weights_poison']
main.logger.info(f"Scaling by {clip_rate}")
for key, value in model.state_dict().items():
target_val = last_params_vars[key]
new_value = target_val + (value -
target_val) * clip_rate
model.state_dict()[key].copy_(new_value)
distance = helper.model_dist_norm(model,
target_params_vars)
main.logger.info(
f'Scaled Norm after poisoning: '
f'{helper.model_global_norm(model)}, distance: {distance}'
)
distance = helper.model_dist_norm(model, target_params_vars)
main.logger.info(
f"Total norm for {curr_adversary_count} "
f"adversaries is: {helper.model_global_norm(model)}. distance: {distance}"
)
else:
for internal_epoch in range(
1, helper.params['internal_epochs'] + 1):
temp_local_epoch += 1
train_data = helper.statehelper_dic[
state_key].get_trainloader()
data_iterator = train_data
total_loss = 0.
correct = 0
data_size = 0
for batch_id, batch in enumerate(data_iterator):
optimizer.zero_grad()
data, targets = helper.statehelper_dic[
state_key].get_batch(data_iterator,
batch,
eval=False)
data_size += len(data)
output = model(data)
loss = F.cross_entropy(output, targets)
loss.backward()
# get gradients
if helper.params[
'aggregation_methods'] == config.AGGR_FOOLSGOLD:
for i, (name, params) in enumerate(
model.named_parameters()):
if params.requires_grad:
if internal_epoch == 1 and batch_id == 0:
client_grad.append(params.grad.clone())
else:
client_grad[i] += params.grad.clone()
optimizer.step()
total_loss += loss.data
pred = output.data.max(1)[
1] # get the index of the max log-probability
correct += pred.eq(
targets.data.view_as(pred)).cpu().sum().item()
acc = 100.0 * (float(correct) / float(data_size))
total_l = total_loss / data_size
main.logger.info(
'_Train {}, epoch {:3d}, local model {}, internal_epoch {:3d}, Avg loss: {:.4f},'
' Accuracy: {}/{} ({:.4f}%)'.format(
model.name, epoch, state_key, internal_epoch,
total_l, correct, data_size, acc))
csv_record.train_result.append([
state_key, temp_local_epoch, epoch, internal_epoch,
total_l.item(), acc, correct, data_size
])
if helper.params['vis_train']:
model.train_vis(main.vis,
temp_local_epoch,
acc,
loss=total_l,
eid=helper.params['environment_name'],
is_poisoned=False,
name=state_key)
num_samples_dict[state_key] = data_size
# test local model after internal epoch train finish
"""
Attributes/Metrics over one epoch
e_correct: no. of correct predictions in this epoch
e_total: total samples in this epoch
"""
e_loss, e_acc, e_correct, e_total = test.Mytest(
helper,
epoch,
model,
is_poison=False,
visualize=True,
agent_name_key=state_key)
csv_record.test_result.append(
[state_key, epoch, e_loss, e_acc, e_correct, e_total])
if is_poison:
if state_key in helper.params['adversary_list'] and (
epoch in localmodel_poison_epochs):
e_loss, e_acc, e_correct, e_total = test.Mytest_poison(
helper,
epoch,
model,
is_poison=True,
visualize=True,
agent_name_key=state_key)
csv_record.poisontest_result.append(
[state_key, epoch, e_loss, e_acc, e_correct, e_total])
# test on local triggers
if state_key in helper.params['adversary_list']:
if helper.params['vis_trigger_split_test']:
model.trigger_agent_test_vis(
main.vis,
epoch,
acc=e_acc,
loss=None,
eid=helper.params['environment_name'],
name=state_key + "_combine")
e_loss, e_acc, e_correct, e_total = test.Mytest_poison_agent_trigger(
helper, model, agent_name_key=state_key)
csv_record.poisontriggertest_result.append([
state_key, state_key + "_trigger", "", epoch, e_loss,
e_acc, e_correct, e_total
])
if helper.params['vis_trigger_split_test']:
model.trigger_agent_test_vis(
main.vis,
epoch,
acc=e_acc,
loss=None,
eid=helper.params['environment_name'],
name=state_key + "_trigger")
# update the weight and bias
local_model_update_dict = dict()
for name, data in model.state_dict().items():
local_model_update_dict[name] = torch.zeros(data.shape)
local_model_update_dict[name] = (data - last_params_vars[name])
last_params_vars[name] = copy.deepcopy(data)
if helper.params['aggregation_methods'] == config.AGGR_FOOLSGOLD:
epochs_local_upd_list.append(client_grad)
else:
epochs_local_upd_list.append(local_model_update_dict)
epochs_submit_upd_dict[state_key] = epochs_local_upd_list
return epochs_submit_upd_dict, num_samples_dict
vis_agg_weight(helper, names, weights, epoch, vis,
adversarial_name_keys)
vis_fg_alpha(helper, names, alphas, epoch, vis,
adversarial_name_keys)
num_oracle_calls = 1
# clear the weight_accumulator
weight_accumulator = helper.init_weight_accumulator(
helper.target_model)
temp_global_epoch = epoch + helper.params['aggr_epoch_interval'] - 1
epoch_loss, epoch_acc, epoch_corret, epoch_total = test.Mytest(
helper=helper,
epoch=temp_global_epoch,
model=helper.target_model,
is_poison=False,
visualize=True,
agent_name_key="global")
csv_record.test_result.append([
"global", temp_global_epoch, epoch_loss, epoch_acc, epoch_corret,
epoch_total
])
if len(csv_record.scale_temp_one_row) > 0:
csv_record.scale_temp_one_row.append(round(epoch_acc, 4))
if helper.params['is_poison']:
epoch_loss, epoch_acc_p, epoch_corret, epoch_total = test.Mytest_poison(
helper=helper,
epoch=temp_global_epoch,
def ImageTrain(helper, start_epoch, local_model, target_model, is_poison,
agent_name_keys):
epochs_submit_update_dict = dict()
num_samples_dict = dict()
current_number_of_adversaries = 0
for temp_name in agent_name_keys:
if temp_name in helper.params['adversary_list']:
current_number_of_adversaries += 1
for model_id in range(helper.params['no_models']):
epochs_local_update_list = []
last_local_model = dict()
client_grad = [] # only works for aggr_epoch_interval=1
for name, data in target_model.state_dict().items():
last_local_model[name] = target_model.state_dict()[name].clone()
agent_name_key = agent_name_keys[model_id]
## Synchronize LR and models
model = local_model
model.copy_params(target_model.state_dict())
optimizer = torch.optim.SGD(model.parameters(),
lr=helper.params['lr'],
momentum=helper.params['momentum'],
weight_decay=helper.params['decay'])
model.train()
adversarial_index = -1
localmodel_poison_epochs = helper.params['poison_epochs']
if is_poison and agent_name_key in helper.params['adversary_list']:
for temp_index in range(0, len(helper.params['adversary_list'])):
if int(agent_name_key
) == helper.params['adversary_list'][temp_index]:
adversarial_index = temp_index
localmodel_poison_epochs = helper.params[str(temp_index) +
'_poison_epochs']
main.logger.info(
f'poison local model {agent_name_key} index {adversarial_index} '
)
break
if len(helper.params['adversary_list']) == 1:
adversarial_index = -1 # the global pattern
for epoch in range(start_epoch,
start_epoch + helper.params['aggr_epoch_interval']):
target_params_variables = dict()
for name, param in target_model.named_parameters():
target_params_variables[name] = last_local_model[name].clone(
).detach().requires_grad_(False)
if epoch in helper.params['generation_epoch'] and helper.params[
'is_generated']:
if adversarial_index == -1:
if model_id == 0:
for i in range(0, helper.params['trigger_num']):
if helper.params['generation_epoch'][i] == epoch:
print("generating_trigger")
generate_trigger(helper, i)
else:
print("generating_trigger")
generate_trigger(helper, adversarial_index)
if is_poison and agent_name_key in helper.params[
'adversary_list'] and (epoch in localmodel_poison_epochs):
main.logger.info('poison_now')
poison_lr = helper.params['poison_lr']
internal_epoch_num = helper.params['internal_poison_epochs']
step_lr = helper.params['poison_step_lr']
poison_optimizer = torch.optim.SGD(
model.parameters(),
lr=poison_lr,
momentum=helper.params['momentum'],
weight_decay=helper.params['decay'])
scheduler = torch.optim.lr_scheduler.MultiStepLR(
poison_optimizer,
milestones=[
0.2 * internal_epoch_num, 0.8 * internal_epoch_num
],
gamma=0.1)
temp_local_epoch = (epoch - 1) * internal_epoch_num
for internal_epoch in range(1, internal_epoch_num + 1):
temp_local_epoch += 1
_, data_iterator = helper.train_data[agent_name_key]
poison_data_count = 0
total_loss = 0.
correct = 0
dataset_size = 0
dis2global_list = []
for batch_id, batch in enumerate(data_iterator):
data, targets, poison_num = helper.get_poison_batch(
batch,
adversarial_index=adversarial_index,
evaluation=False)
poison_optimizer.zero_grad()
dataset_size += len(data)
poison_data_count += poison_num
output = model(data)
class_loss = nn.functional.cross_entropy(
output, targets)
distance_loss = helper.model_dist_norm_var(
model, target_params_variables)
# Lmodel = αLclass + (1 − α)Lano; alpha_loss =1 fixed
loss = helper.params['alpha_loss'] * class_loss + \
(1 - helper.params['alpha_loss']) * distance_loss
loss.backward()
# get gradients
if helper.params[
'aggregation_methods'] == config.AGGR_FOOLSGOLD:
for i, (name, params) in enumerate(
model.named_parameters()):
if params.requires_grad:
if internal_epoch == 1 and batch_id == 0:
client_grad.append(params.grad.clone())
else:
client_grad[i] += params.grad.clone()
poison_optimizer.step()
total_loss += loss.data
pred = output.data.max(1)[
1] # get the index of the max log-probability
correct += pred.eq(
targets.data.view_as(pred)).cpu().sum().item()
if helper.params["batch_track_distance"]:
# we can calculate distance to this model now.
temp_data_len = len(data_iterator)
distance_to_global_model = helper.model_dist_norm(
model, target_params_variables)
dis2global_list.append(distance_to_global_model)
model.track_distance_batch_vis(
vis=main.vis,
epoch=temp_local_epoch,
data_len=temp_data_len,
batch=batch_id,
distance_to_global_model=
distance_to_global_model,
eid=helper.params['environment_name'],
name=str(agent_name_key),
is_poisoned=True)
if step_lr:
scheduler.step()
main.logger.info(f'Current lr: {scheduler.get_lr()}')
acc = 100.0 * (float(correct) / float(dataset_size))
total_l = total_loss / dataset_size
main.logger.info(
'___PoisonTrain {} , epoch {:3d}, local model {}, internal_epoch {:3d}, Average loss: {:.4f}, '
'Accuracy: {}/{} ({:.4f}%), train_poison_data_count: {}'
.format(model.name, epoch, agent_name_key,
internal_epoch, total_l, correct, dataset_size,
acc, poison_data_count))
csv_record.train_result.append([
agent_name_key, temp_local_epoch, epoch,
internal_epoch,
total_l.item(), acc, correct, dataset_size
])
if helper.params['vis_train']:
model.train_vis(main.vis,
temp_local_epoch,
acc,
loss=total_l,
eid=helper.params['environment_name'],
is_poisoned=True,
name=str(agent_name_key))
num_samples_dict[agent_name_key] = dataset_size
if helper.params["batch_track_distance"]:
main.logger.info(
f'MODEL {model_id}. P-norm is {helper.model_global_norm(model):.4f}. '
f'Distance to the global model: {dis2global_list}. '
)
# internal epoch finish
main.logger.info(
f'Global model norm: {helper.model_global_norm(target_model)}.'
)
main.logger.info(
f'Norm before scaling: {helper.model_global_norm(model)}. '
f'Distance: {helper.model_dist_norm(model, target_params_variables)}'
)
if not helper.params['baseline']:
main.logger.info(f'will scale.')
epoch_loss, epoch_acc, epoch_corret, epoch_total = test.Mytest(
helper=helper,
epoch=epoch,
model=model,
is_poison=False,
visualize=False,
agent_name_key=agent_name_key)
csv_record.test_result.append([
agent_name_key, epoch, epoch_loss, epoch_acc,
epoch_corret, epoch_total
])
epoch_loss, epoch_acc, epoch_corret, epoch_total = test.Mytest_poison(
helper=helper,
epoch=epoch,
model=model,
is_poison=True,
visualize=False,
agent_name_key=agent_name_key)
csv_record.posiontest_result.append([
agent_name_key, epoch, epoch_loss, epoch_acc,
epoch_corret, epoch_total
])
clip_rate = helper.params['scale_weights_poison']
main.logger.info(f"Scaling by {clip_rate}")
for key, value in model.state_dict().items():
target_value = last_local_model[key]
new_value = target_value + (value -
target_value) * clip_rate
model.state_dict()[key].copy_(new_value)
distance = helper.model_dist_norm(model,
target_params_variables)
main.logger.info(
f'Scaled Norm after poisoning: '
f'{helper.model_global_norm(model)}, distance: {distance}'
)
csv_record.scale_temp_one_row.append(epoch)
csv_record.scale_temp_one_row.append(round(distance, 4))
if helper.params["batch_track_distance"]:
temp_data_len = len(
helper.train_data[agent_name_key][1])
model.track_distance_batch_vis(
vis=main.vis,
epoch=temp_local_epoch,
data_len=temp_data_len,
batch=temp_data_len - 1,
distance_to_global_model=distance,
eid=helper.params['environment_name'],
name=str(agent_name_key),
is_poisoned=True)
distance = helper.model_dist_norm(model,
target_params_variables)
main.logger.info(
f"Total norm for {current_number_of_adversaries} "
f"adversaries is: {helper.model_global_norm(model)}. distance: {distance}"
)
else:
temp_local_epoch = (epoch -
1) * helper.params['internal_epochs']
for internal_epoch in range(
1, helper.params['internal_epochs'] + 1):
temp_local_epoch += 1
_, data_iterator = helper.train_data[agent_name_key]
total_loss = 0.
correct = 0
dataset_size = 0
dis2global_list = []
for batch_id, batch in enumerate(data_iterator):
optimizer.zero_grad()
data, targets = helper.get_batch(data_iterator,
batch,
evaluation=False)
dataset_size += len(data)
output = model(data)
loss = nn.functional.cross_entropy(output, targets)
loss.backward()
# get gradients
if helper.params[
'aggregation_methods'] == config.AGGR_FOOLSGOLD:
for i, (name, params) in enumerate(
model.named_parameters()):
if params.requires_grad:
if internal_epoch == 1 and batch_id == 0:
client_grad.append(params.grad.clone())
else:
client_grad[i] += params.grad.clone()
optimizer.step()
total_loss += loss.data
pred = output.data.max(1)[
1] # get the index of the max log-probability
correct += pred.eq(
targets.data.view_as(pred)).cpu().sum().item()
if helper.params["vis_train_batch_loss"]:
cur_loss = loss.data
temp_data_len = len(data_iterator)
model.train_batch_vis(
vis=main.vis,
epoch=temp_local_epoch,
data_len=temp_data_len,
batch=batch_id,
loss=cur_loss,
eid=helper.params['environment_name'],
name=str(agent_name_key),
win='train_batch_loss',
is_poisoned=False)
if helper.params["batch_track_distance"]:
# we can calculate distance to this model now
temp_data_len = len(data_iterator)
distance_to_global_model = helper.model_dist_norm(
model, target_params_variables)
dis2global_list.append(distance_to_global_model)
model.track_distance_batch_vis(
vis=main.vis,
epoch=temp_local_epoch,
data_len=temp_data_len,
batch=batch_id,
distance_to_global_model=
distance_to_global_model,
eid=helper.params['environment_name'],
name=str(agent_name_key),
is_poisoned=False)
acc = 100.0 * (float(correct) / float(dataset_size))
total_l = total_loss / dataset_size
main.logger.info(
'___Train {}, epoch {:3d}, local model {}, internal_epoch {:3d}, Average loss: {:.4f}, '
'Accuracy: {}/{} ({:.4f}%)'.format(
model.name, epoch, agent_name_key, internal_epoch,
total_l, correct, dataset_size, acc))
csv_record.train_result.append([
agent_name_key, temp_local_epoch, epoch,
internal_epoch,
total_l.item(), acc, correct, dataset_size
])
if helper.params['vis_train']:
model.train_vis(main.vis,
temp_local_epoch,
acc,
loss=total_l,
eid=helper.params['environment_name'],
is_poisoned=False,
name=str(agent_name_key))
num_samples_dict[agent_name_key] = dataset_size
if helper.params["batch_track_distance"]:
main.logger.info(
f'MODEL {model_id}. P-norm is {helper.model_global_norm(model):.4f}. '
f'Distance to the global model: {dis2global_list}. '
)
# test local model after internal epoch finishing
epoch_loss, epoch_acc, epoch_corret, epoch_total = test.Mytest(
helper=helper,
epoch=epoch,
model=model,
is_poison=False,
visualize=True,
agent_name_key=agent_name_key)
csv_record.test_result.append([
agent_name_key, epoch, epoch_loss, epoch_acc, epoch_corret,
epoch_total
])
if is_poison:
if agent_name_key in helper.params['adversary_list'] and (
epoch in localmodel_poison_epochs):
epoch_loss, epoch_acc, epoch_corret, epoch_total = test.Mytest_poison(
helper=helper,
epoch=epoch,
model=model,
is_poison=True,
visualize=True,
agent_name_key=agent_name_key)
csv_record.posiontest_result.append([
agent_name_key, epoch, epoch_loss, epoch_acc,
epoch_corret, epoch_total
])
# test on local triggers
if agent_name_key in helper.params['adversary_list']:
if helper.params['vis_trigger_split_test']:
model.trigger_agent_test_vis(
vis=main.vis,
epoch=epoch,
acc=epoch_acc,
loss=None,
eid=helper.params['environment_name'],
name=str(agent_name_key) + "_combine")
epoch_loss, epoch_acc, epoch_corret, epoch_total = \
test.Mytest_poison_agent_trigger(helper=helper, model=model, agent_name_key=agent_name_key)
csv_record.poisontriggertest_result.append([
agent_name_key,
str(agent_name_key) + "_trigger", "", epoch,
epoch_loss, epoch_acc, epoch_corret, epoch_total
])
if helper.params['vis_trigger_split_test']:
model.trigger_agent_test_vis(
vis=main.vis,
epoch=epoch,
acc=epoch_acc,
loss=None,
eid=helper.params['environment_name'],
name=str(agent_name_key) + "_trigger")
# update the model weight
local_model_update_dict = dict()
for name, data in model.state_dict().items():
local_model_update_dict[name] = torch.zeros_like(data)
local_model_update_dict[name] = (data - last_local_model[name])
last_local_model[name] = copy.deepcopy(data)
if helper.params['aggregation_methods'] == config.AGGR_FOOLSGOLD:
epochs_local_update_list.append(client_grad)
else:
epochs_local_update_list.append(local_model_update_dict)
epochs_submit_update_dict[agent_name_key] = epochs_local_update_list
return epochs_submit_update_dict, num_samples_dict
max_upd_norm=config.MAX_UPDATE_NORM)
vis_agg_weight(helper, names, weights, epoch, vis, adversarial_name_keys)
vis_fg_alpha(helper, names, alphas, epoch, vis, adversarial_name_keys)
elif helper.params['aggregation_methods'] == config.AGGR_FOOLSGOLD:
is_updated, names, weights, alphas = helper.foolsgold_update(helper.target_model, updates)
vis_agg_weight(helper, names, weights, epoch, vis, adversarial_name_keys)
vis_fg_alpha(helper, names, alphas, epoch, vis, adversarial_name_keys)
num_oracle_calls = 1
# clear the weight_accumulator
weight_accumulator = helper.init_weight_accumulator(helper.target_model)
temp_global_epoch = epoch + helper.params['aggr_epoch_interval'] - 1
e_loss, e_acc, e_correct, e_total = test.Mytest(helper, temp_global_epoch, helper.target_model,
is_poison=False, visualize=True, agent_name_key="global")
csv_record.test_result.append(["global", temp_global_epoch, e_loss, e_acc, e_correct, e_total])
if len(csv_record.scale_temp_one_row) > 0:
csv_record.scale_temp_one_row.append(round(e_acc, 4))
if helper.params['is_poison']:
e_loss, e_acc_p, e_correct, e_total = test.Mytest_poison(helper, temp_global_epoch, helper.target_model,
is_poison=True, visualize=True, agent_name_key="global")
csv_record.poisontest_result.append(["global", temp_global_epoch, e_loss, e_acc_p, e_correct, e_total])
# test on local triggers
csv_record.poisontriggertest_result.append(["global", "combine", "", temp_global_epoch, e_loss,
