def train(self):
print('---{} workers per communication round---'.format(
self.clients_per_round))
np.random.seed(1234567 + self.seed)
corrupt_id = np.random.choice(range(len(self.clients)),
size=self.num_corrupted,
replace=False)
print(corrupt_id)
if self.dataset == 'shakespeare':
for c in self.clients:
c.train_data['y'], c.train_data['x'] = process_y(
c.train_data['y']), process_x(c.train_data['x'])
c.test_data['y'], c.test_data['x'] = process_y(
c.test_data['y']), process_x(c.test_data['x'])
batches = {}
for idx, c in enumerate(self.clients):
if idx in corrupt_id:
c.train_data['y'] = np.asarray(c.train_data['y'])
if self.dataset == 'celeba':
c.train_data['y'] = 1 - c.train_data['y']
elif self.dataset == 'femnist':
c.train_data['y'] = np.random.randint(
0, 62, len(c.train_data['y'])) # [0, 62)
elif self.dataset == 'shakespeare':
c.train_data['y'] = np.random.randint(
0, 80, len(c.train_data['y']))
elif self.dataset == "vehicle":
c.train_data['y'] = c.train_data['y'] * -1
elif self.dataset == "fmnist":
c.train_data['y'] = np.random.randint(
0, 10, len(c.train_data['y']))
if self.dataset == 'celeba':
# due to a different data storage format
batches[c] = gen_batch_celeba(
c.train_data, self.batch_size,
self.num_rounds * self.local_iters)
else:
batches[c] = gen_batch(c.train_data, self.batch_size,
self.num_rounds * self.local_iters)
for i in range(self.num_rounds + 1):
if i % self.eval_every == 0 and i > 0:
tmp_models = []
for idx in range(len(self.clients)):
tmp_models.append(self.local_models[idx])
num_train, num_correct_train, loss_vector = self.train_error(
tmp_models)
avg_train_loss = np.dot(loss_vector,
num_train) / np.sum(num_train)
num_test, num_correct_test, _ = self.test(tmp_models)
tqdm.write('At round {} training accu: {}, loss: {}'.format(
i,
np.sum(num_correct_train) * 1.0 / np.sum(num_train),
avg_train_loss))
tqdm.write('At round {} test accu: {}'.format(
i,
np.sum(num_correct_test) * 1.0 / np.sum(num_test)))
non_corrupt_id = np.setdiff1d(range(len(self.clients)),
corrupt_id)
tqdm.write('At round {} malicious test accu: {}'.format(
i,
np.sum(num_correct_test[corrupt_id]) * 1.0 /
np.sum(num_test[corrupt_id])))
tqdm.write('At round {} benign test accu: {}'.format(
i,
np.sum(num_correct_test[non_corrupt_id]) * 1.0 /
np.sum(num_test[non_corrupt_id])))
print(
"variance of the performance: ",
np.var(num_correct_test[non_corrupt_id] /
num_test[non_corrupt_id]))
# weighted sampling
indices, selected_clients = self.select_clients(
round=i, num_clients=self.clients_per_round)
csolns = []
losses = []
for idx in indices:
w_global_idx = copy.deepcopy(self.global_model)
c = self.clients[idx]
for _ in range(self.local_iters):
data_batch = next(batches[c])
# local
self.client_model.set_params(self.local_models[idx])
_, grads, _ = c.solve_sgd(data_batch)
if self.dynamic_lam:
model_tmp = copy.deepcopy(self.local_models[idx])
model_best = copy.deepcopy(self.local_models[idx])
tmp_loss = 10000
# pick a lambda locally based on validation data
for lam_id, candidate_lam in enumerate([0.1, 1, 2]):
for layer in range(len(grads[1])):
eff_grad = grads[1][layer] + candidate_lam * (
self.local_models[idx][layer] -
self.global_model[layer])
model_tmp[layer] = self.local_models[idx][
layer] - self.learning_rate * eff_grad
c.set_params(model_tmp)
l = c.get_val_loss()
if l < tmp_loss:
tmp_loss = l
model_best = copy.deepcopy(model_tmp)
self.local_models[idx] = copy.deepcopy(model_best)
else:
for layer in range(len(grads[1])):
eff_grad = grads[1][layer] + self.lam * (
self.local_models[idx][layer] -
self.global_model[layer])
self.local_models[idx][layer] = self.local_models[
idx][layer] - self.learning_rate * eff_grad
# global
self.client_model.set_params(w_global_idx)
loss = c.get_loss()
losses.append(loss)
_, grads, _ = c.solve_sgd(data_batch)
w_global_idx = self.client_model.get_params()
# get the difference (global model updates)
diff = [
u - v for (u, v) in zip(w_global_idx, self.global_model)
]
# send the malicious updates
if idx in corrupt_id:
if self.boosting:
# scale malicious updates
diff = [self.clients_per_round * u for u in diff]
elif self.random_updates:
# send random updates
stdev_ = get_stdev(diff)
diff = [
np.random.normal(0, stdev_, size=u.shape)
for u in diff
]
if self.q == 0:
csolns.append(diff)
else:
csolns.append((np.exp(self.q * loss), diff))
if self.q != 0:
avg_updates = self.aggregate(csolns)
else:
if self.gradient_clipping:
csolns = l2_clip(csolns)
expected_num_mali = int(self.clients_per_round *
self.num_corrupted / len(self.clients))
if self.median:
avg_updates = self.median_average(csolns)
elif self.k_norm:
avg_updates = self.k_norm_average(
self.clients_per_round - expected_num_mali, csolns)
elif self.krum:
avg_updates = self.krum_average(
self.clients_per_round - expected_num_mali - 2, csolns)
elif self.mkrum:
m = self.clients_per_round - expected_num_mali
avg_updates = self.mkrum_average(
self.clients_per_round - expected_num_mali - 2, m,
csolns)
else:
avg_updates = self.simple_average(csolns)
# update the global model
for layer in range(len(avg_updates)):
self.global_model[layer] += avg_updates[layer]
def train(self):
print('Training with {} workers ---'.format(self.clients_per_round))
np.random.seed(1234567 + self.seed)
corrupt_id = np.random.choice(range(len(self.clients)),
size=self.num_corrupted,
replace=False)
print(corrupt_id)
batches = {}
for idx, c in enumerate(self.clients):
if idx in corrupt_id:
c.train_data['y'] = np.asarray(c.train_data['y'])
if self.dataset == 'celeba':
c.train_data['y'] = 1 - c.train_data['y']
elif self.dataset == 'femnist':
c.train_data['y'] = np.random.randint(
0, 62, len(c.train_data['y'])) # [0, 62)
elif self.dataset == 'fmnist': # fashion mnist
c.train_data['y'] = np.random.randint(
0, 10, len(c.train_data['y']))
if self.dataset == 'celeba':
batches[c] = gen_batch_celeba(
c.train_data, self.batch_size,
self.num_rounds * self.local_iters + 350)
else:
batches[c] = gen_batch(
c.train_data, self.batch_size,
self.num_rounds * self.local_iters + 350)
initialization = copy.deepcopy(self.clients[0].get_params())
for i in range(self.num_rounds + 1):
if i % self.eval_every == 0:
num_test, num_correct_test, _ = self.test(
) # have set the latest model for all clients
num_train, num_correct_train, loss_vector = self.train_error()
avg_loss = np.dot(loss_vector, num_train) / np.sum(num_train)
tqdm.write('At round {} training accu: {}, loss: {}'.format(
i,
np.sum(num_correct_train) * 1.0 / np.sum(num_train),
avg_loss))
tqdm.write('At round {} test accu: {}'.format(
i,
np.sum(num_correct_test) * 1.0 / np.sum(num_test)))
non_corrupt_id = np.setdiff1d(range(len(self.clients)),
corrupt_id)
tqdm.write('At round {} malicious test accu: {}'.format(
i,
np.sum(num_correct_test[corrupt_id]) * 1.0 /
np.sum(num_test[corrupt_id])))
tqdm.write('At round {} benign test accu: {}'.format(
i,
np.sum(num_correct_test[non_corrupt_id]) * 1.0 /
np.sum(num_test[non_corrupt_id])))
print(
"variance of the performance: ",
np.var(num_correct_test[non_corrupt_id] /
num_test[non_corrupt_id]))
indices, selected_clients = self.select_clients(
round=i,
corrupt_id=corrupt_id,
num_clients=self.clients_per_round)
csolns = []
losses = []
for idx in indices:
c = self.clients[idx]
# communicate the latest model
c.set_params(self.latest_model)
weights_before = copy.deepcopy(self.latest_model)
loss = c.get_loss() # compute loss on the whole training data
losses.append(loss)
for _ in range(self.local_iters):
data_batch = next(batches[c])
_, _, _ = c.solve_sgd(data_batch)
new_weights = c.get_params()
grads = [(u - v) * 1.0
for u, v in zip(new_weights, weights_before)]
if idx in corrupt_id:
if self.boosting: # model replacement
grads = [self.clients_per_round * u for u in grads]
elif self.random_updates:
# send random updates
stdev_ = get_stdev(grads)
grads = [
np.random.normal(0, stdev_, size=u.shape)
for u in grads
]
if self.q > 0:
csolns.append((np.exp(self.q * loss), grads))
else:
csolns.append(grads)
if self.q > 0:
overall_updates = self.aggregate(csolns)
else:
if self.gradient_clipping:
csolns = l2_clip(csolns)
expected_num_mali = int(self.clients_per_round *
self.num_corrupted / len(self.clients))
if self.median:
overall_updates = self.median_average(csolns)
elif self.k_norm:
overall_updates = self.k_norm_average(
self.clients_per_round - expected_num_mali, csolns)
elif self.k_loss:
overall_updates = self.k_loss_average(
self.clients_per_round - expected_num_mali, losses,
csolns)
elif self.krum:
overall_updates = self.krum_average(
self.clients_per_round - expected_num_mali - 2, csolns)
elif self.mkrum:
m = self.clients_per_round - expected_num_mali
overall_updates = self.mkrum_average(
self.clients_per_round - expected_num_mali - 2, m,
csolns)
else:
overall_updates = self.simple_average(csolns)
self.latest_model = [
(u + v) for u, v in zip(self.latest_model, overall_updates)
]
distance = np.linalg.norm(
process_grad(self.latest_model) - process_grad(initialization))
if i % self.eval_every == 0:
print('distance to initialization:', distance)
# local finetuning
init_model = copy.deepcopy(self.latest_model)
after_test_accu = []
test_samples = []
for idx, c in enumerate(self.clients):
c.set_params(init_model)
local_model = copy.deepcopy(init_model)
for _ in range(
max(
int(self.finetune_iters * c.train_samples /
self.batch_size), self.finetune_iters)):
c.set_params(local_model)
data_batch = next(batches[c])
_, grads, _ = c.solve_sgd(data_batch)
for j in range(len(grads[1])):
eff_grad = grads[1][j] + self.lam * (local_model[j] -
init_model[j])
local_model[j] = local_model[
j] - self.learning_rate * self.decay_factor * eff_grad
c.set_params(local_model)
tc, _, num_test = c.test()
after_test_accu.append(tc)
test_samples.append(num_test)
after_test_accu = np.asarray(after_test_accu)
test_samples = np.asarray(test_samples)
tqdm.write('final test accu: {}'.format(
np.sum(after_test_accu) * 1.0 / np.sum(test_samples)))
tqdm.write('final malicious test accu: {}'.format(
np.sum(after_test_accu[corrupt_id]) * 1.0 /
np.sum(test_samples[corrupt_id])))
tqdm.write('final benign test accu: {}'.format(
np.sum(after_test_accu[non_corrupt_id]) * 1.0 /
np.sum(test_samples[non_corrupt_id])))
print(
"variance of the performance: ",
np.var(after_test_accu[non_corrupt_id] /
test_samples[non_corrupt_id]))
def train(self):
print('Training with {} workers ---'.format(self.clients_per_round))
np.random.seed(1234567 + self.seed)
corrupt_id = np.random.choice(range(len(self.clients)),
size=self.num_corrupted,
replace=False)
print(corrupt_id)
if self.dataset == 'shakespeare':
for c in self.clients:
c.train_data['y'], c.train_data['x'] = process_y(
c.train_data['y']), process_x(c.train_data['x'])
c.test_data['y'], c.test_data['x'] = process_y(
c.test_data['y']), process_x(c.test_data['x'])
batches = {}
for idx, c in enumerate(self.clients):
if idx in corrupt_id:
c.train_data['y'] = np.asarray(c.train_data['y'])
if self.dataset == 'celeba':
c.train_data['y'] = 1 - c.train_data['y']
elif self.dataset == 'femnist':
c.train_data['y'] = np.random.randint(
0, 62, len(c.train_data['y']))
elif self.dataset == 'shakespeare':
c.train_data['y'] = np.random.randint(
0, 80, len(c.train_data['y']))
elif self.dataset == "vehicle":
c.train_data['y'] = c.train_data['y'] * -1
elif self.dataset == 'fmnist':
c.train_data['y'] = np.random.randint(
0, 10, len(c.train_data['y']))
if self.dataset == 'celeba': # need to deal with celeba data loading a bit differently
batches[c] = gen_batch_celeba(
c.train_data, self.batch_size,
self.num_rounds * self.local_iters)
else:
batches[c] = gen_batch(c.train_data, self.batch_size,
self.num_rounds * self.local_iters)
for i in range(self.num_rounds + 1):
if i % self.eval_every == 0:
num_test, num_correct_test, test_loss_vector = self.test(
) # have set the latest model for all clients
avg_test_loss = np.dot(test_loss_vector,
num_test) / np.sum(num_test)
num_train, num_correct_train, train_loss_vector = self.train_error(
)
avg_train_loss = np.dot(train_loss_vector,
num_train) / np.sum(num_train)
tqdm.write('At round {} training accu: {}, loss: {}'.format(
i,
np.sum(num_correct_train) * 1.0 / np.sum(num_train),
avg_train_loss))
tqdm.write('At round {} test loss: {}'.format(
i, avg_test_loss))
tqdm.write('At round {} test accu: {}'.format(
i,
np.sum(num_correct_test) * 1.0 / np.sum(num_test)))
non_corrupt_id = np.setdiff1d(range(len(self.clients)),
corrupt_id)
tqdm.write('At round {} malicious test accu: {}'.format(
i,
np.sum(num_correct_test[corrupt_id]) * 1.0 /
np.sum(num_test[corrupt_id])))
tqdm.write('At round {} benign test accu: {}'.format(
i,
np.sum(num_correct_test[non_corrupt_id]) * 1.0 /
np.sum(num_test[non_corrupt_id])))
tqdm.write('At round {} variance: {}'.format(
i,
np.var(num_correct_test[non_corrupt_id] * 1.0 /
num_test[non_corrupt_id])))
indices, selected_clients = self.select_clients(
round=i,
corrupt_id=corrupt_id,
num_clients=self.clients_per_round)
csolns = []
losses = []
for idx in indices:
c = self.clients[idx]
# communicate the latest model
c.set_params(self.latest_model)
weights_before = copy.deepcopy(self.latest_model)
loss = c.get_loss() # training loss
losses.append(loss)
for _ in range(self.local_iters):
data_batch = next(batches[c])
_, grads, _ = c.solve_sgd(data_batch)
w_global = c.get_params()
grads = [(u - v) * 1.0
for u, v in zip(w_global, weights_before)]
if idx in corrupt_id:
if self.boosting: # model replacement
grads = [self.clients_per_round * u for u in grads]
elif self.random_updates:
# send random updates
stdev_ = get_stdev(grads)
grads = [
np.random.normal(0, stdev_, size=u.shape)
for u in grads
]
if self.q == 0:
csolns.append(grads)
else:
csolns.append((np.exp(self.q * loss), grads))
if self.q != 0:
overall_updates = self.aggregate(csolns)
else:
if self.gradient_clipping:
csolns = l2_clip(csolns)
expected_num_mali = int(self.clients_per_round *
self.num_corrupted / len(self.clients))
if self.median:
overall_updates = self.median_average(csolns)
elif self.k_norm:
overall_updates = self.k_norm_average(
self.clients_per_round - expected_num_mali, csolns)
elif self.k_loss:
overall_updates = self.k_loss_average(
self.clients_per_round - expected_num_mali, losses,
csolns)
elif self.krum:
overall_updates = self.krum_average(
self.clients_per_round - expected_num_mali - 2, csolns)
elif self.mkrum:
m = self.clients_per_round - expected_num_mali
overall_updates = self.mkrum_average(
self.clients_per_round - expected_num_mali - 2, m,
csolns)
elif self.fedmgda:
overall_updates = self.fedmgda_average(csolns)
else:
overall_updates = self.simple_average(csolns)
self.latest_model = [
(u + v) for u, v in zip(self.latest_model, overall_updates)
]
def train(self):
print('---{} workers per communication round---'.format(
self.clients_per_round))
np.random.seed(1234567)
corrupt_id = np.random.choice(range(len(self.clients)),
size=self.num_corrupted)
print(corrupt_id)
batches = {}
for idx, c in enumerate(self.clients):
if idx in corrupt_id:
c.train_data['y'] = np.asarray(c.train_data['y'])
if self.dataset == 'celeba':
c.train_data['y'] = 1 - c.train_data['y']
elif self.dataset == 'femnist':
c.train_data['y'] = np.random.randint(
0, 62, len(c.train_data['y'])) # [0, 62)
elif self.dataset == 'shakespeare':
c.train_data['y'] = np.random.randint(
0, 80, len(c.train_data['y']))
elif self.dataset == "vehicle":
c.train_data['y'] = c.train_data['y'] * -1
elif self.dataset == "fmnist":
c.train_data['y'] = np.random.randint(
0, 10, len(c.train_data['y']))
if self.dataset == 'celeba':
# due to a different data storage format
batches[c] = gen_batch_celeba(
c.train_data, self.batch_size,
self.num_rounds * self.local_iters)
else:
batches[c] = gen_batch(c.train_data, self.batch_size,
self.num_rounds * self.local_iters)
initialization = copy.deepcopy(self.clients[0].get_params())
for i in range(self.num_rounds + 1):
if i % self.eval_every == 0 and i > 0:
tmp_models = []
for idx in range(len(self.clients)):
a = []
for layer in range(len(self.local_models[idx])):
a.append(self.alpha * self.local_models[idx][layer] +
(1 - self.alpha) * self.global_model[layer])
tmp_models.append(a)
num_test, num_correct_test = self.test(tmp_models)
num_train, num_correct_train, loss_vector = self.train_error(
tmp_models)
avg_loss = np.dot(loss_vector, num_train) / np.sum(num_train)
print(num_correct_test / num_test)
tqdm.write('At round {} training accu: {}, loss: {}'.format(
i,
np.sum(num_correct_train) * 1.0 / np.sum(num_train),
avg_loss))
tqdm.write('At round {} test accu: {}'.format(
i,
np.sum(num_correct_test) * 1.0 / np.sum(num_test)))
non_corrupt_id = np.setdiff1d(range(len(self.clients)),
corrupt_id)
tqdm.write('At round {} malicious test accu: {}'.format(
i,
np.sum(num_correct_test[corrupt_id]) * 1.0 /
np.sum(num_test[corrupt_id])))
tqdm.write('At round {} benign test accu: {}'.format(
i,
np.sum(num_correct_test[non_corrupt_id]) * 1.0 /
np.sum(num_test[non_corrupt_id])))
print(
"variance of the performance: ",
np.var(num_correct_test[non_corrupt_id] /
num_test[non_corrupt_id]))
# weighted sampling
indices, selected_clients = self.select_clients(
round=i, num_clients=self.clients_per_round)
csolns = []
for idx in indices:
c = self.clients[idx]
# server sends the current global model to selected devices
w_global_idx = copy.deepcopy(self.global_model)
self.client_model.set_params(self.global_model)
for _ in range(self.local_iters):
# first sample a mini-batch
data_batch = next(batches[c])
# optimize the global model
self.client_model.set_params(w_global_idx)
_, grads, _ = c.solve_sgd(
data_batch) # grads: (num_samples, real_grads)
w_global_idx = self.client_model.get_params()
# optimize for the local model (wrt to the interpolation)
self.client_model.set_params(self.interpolation[idx])
_, grads, _ = c.solve_sgd(data_batch)
for layer in range(len(self.local_models[idx])):
self.local_models[idx][layer] = self.local_models[idx][
layer] - self.alpha * self.learning_rate * grads[
1][layer]
#
# update the interpolation
for layer in range(len(self.local_models[idx])):
self.interpolation[idx][
layer] = self.alpha * self.local_models[idx][
layer] + (1 - self.alpha) * w_global_idx[layer]
diff = [
u - v for (u, v) in zip(w_global_idx, self.global_model)
]
# send the malicious updates
if idx in corrupt_id:
if self.boosting:
# scale malicious updates
diff = [self.clients_per_round * u for u in diff]
elif self.random_updates:
# send random updates
stdev_ = get_stdev(diff)
diff = [
np.random.normal(0, stdev_, size=u.shape)
for u in diff
]
csolns.append(diff)
if self.gradient_clipping:
csolns = l2_clip(csolns)
if self.median:
avg_updates = self.median_average(csolns)
else:
avg_updates = self.simple_average(csolns)
for layer in range(len(avg_updates)):
self.global_model[layer] += avg_updates[layer]
def train(self):
print('---{} workers per communication round---'.format(self.clients_per_round))
np.random.seed(1234567+self.seed)
corrupt_id = np.random.choice(range(len(self.clients)), size=self.num_corrupted, replace=False)
batches = {}
for idx, c in enumerate(self.clients):
if idx in corrupt_id:
c.train_data['y'] = np.asarray(c.train_data['y'])
if self.dataset == 'celeba':
c.train_data['y'] = 1 - c.train_data['y']
elif self.dataset == 'femnist':
c.train_data['y'] = np.random.randint(0, 62, len(c.train_data['y']))
if self.dataset == 'celeba':
batches[c] = gen_batch_celeba(c.train_data, self.batch_size, self.num_rounds * self.local_iters + 300)
else:
batches[c] = gen_batch(c.train_data, self.batch_size, self.num_rounds * self.local_iters + 300)
for i in range(self.num_rounds + 1):
if i % self.eval_every == 0 and i > 0:
num_test, num_correct_test, _ = self.test() # have set the latest model for all clients
num_train, num_correct_train, loss_vector = self.train_error()
avg_loss = np.dot(loss_vector, num_train) / np.sum(num_train)
tqdm.write('At round {} training accu: {}, loss: {}'.format(i, np.sum(num_correct_train) * 1.0 / np.sum(
num_train), avg_loss))
tqdm.write('At round {} test accu: {}'.format(i, np.sum(num_correct_test) * 1.0 / np.sum(num_test)))
non_corrupt_id = np.setdiff1d(range(len(self.clients)), corrupt_id)
tqdm.write('At round {} malicious test accu: {}'.format(i, np.sum(
num_correct_test[corrupt_id]) * 1.0 / np.sum(num_test[corrupt_id])))
tqdm.write('At round {} benign test accu: {}'.format(i, np.sum(
num_correct_test[non_corrupt_id]) * 1.0 / np.sum(num_test[non_corrupt_id])))
print("variance of the performance: ",
np.var(num_correct_test[non_corrupt_id] / num_test[non_corrupt_id]))
# weighted sampling
indices, selected_clients = self.select_clients(round=i, corrupt_id=corrupt_id, num_clients=self.clients_per_round)
csolns = []
for idx in indices:
w_global_idx = copy.deepcopy(self.latest_model)
c = self.clients[idx]
c.set_params(w_global_idx)
for _ in range(self.local_iters):
data_batch = next(batches[c])
_, grads, _ = c.solve_sgd(data_batch)
w_global_idx = self.client_model.get_params()
# get the difference (global model updates)
diff = [u - v for (u, v) in zip(w_global_idx, self.latest_model)]
# send the malicious updates
if idx in corrupt_id:
if self.boosting:
# scale malicious updates
diff = [self.clients_per_round * u for u in diff]
elif self.random_updates:
# send random updates
stdev_ = get_stdev(diff)
diff = [np.random.normal(0, stdev_, size=u.shape) for u in diff]
csolns.append(diff)
if self.gradient_clipping:
csolns = l2_clip(csolns)
avg_updates = self.simple_average(csolns)
# update the global model
for layer in range(len(avg_updates)):
self.latest_model[layer] += avg_updates[layer]
# local finetuning based on KL
after_test_accu = []
test_samples = []
for idx, c in enumerate(self.clients):
c.set_params(self.latest_model)
output2 = copy.deepcopy(c.get_softmax())
# start to finetune
local_model = copy.deepcopy(self.latest_model)
for _ in range(max(int(self.finetune_iters * c.train_samples / self.batch_size), self.finetune_iters)):
data_batch = next(batches[c])
c.set_params(local_model)
kl_grads = c.get_kl_grads(output2)
_, grads, _ = c.solve_sgd(data_batch)
for j in range(len(grads[1])):
eff_grad = grads[1][j] + self.lam * kl_grads[j]
local_model[j] = local_model[j] - self.learning_rate * eff_grad
c.set_params(local_model)
tc, _, num_test = c.test()
after_test_accu.append(tc)
test_samples.append(num_test)
non_corrupt_id = np.setdiff1d(range(len(self.clients)), corrupt_id)
after_test_accu = np.asarray(after_test_accu)
test_samples = np.asarray(test_samples)
tqdm.write('final test accu: {}'.format(np.sum(after_test_accu) * 1.0 / np.sum(test_samples)))
tqdm.write('final malicious test accu: {}'.format(np.sum(
after_test_accu[corrupt_id]) * 1.0 / np.sum(test_samples[corrupt_id])))
tqdm.write('final benign test accu: {}'.format(np.sum(
after_test_accu[non_corrupt_id]) * 1.0 / np.sum(test_samples[non_corrupt_id])))
print("variance of the performance: ",
np.var(after_test_accu[non_corrupt_id] / test_samples[non_corrupt_id]))