noise = torch.from_numpy(
sts.laplace.rvs(
loc=0,
scale=0.375,
size=clients_grad_dict[0][key].shape)).type(
torch.float).to(torch.device("cpu"))
if c < 15:
noise = noise0
t = clients_grad_dict[c][key] + noise
temp.append(t)
if c > 14:
client_average_grad_dict[key] += t / 50
t_vector.append(
np.concatenate((np.array(t.view(1, -1))), axis=0))
# '''
hv[epoch] = t_vector.copy()
if key == 'fc3.weight':
for l in range(len(t_vector)):
for n in range(epoch - 1):
t_vector[l] += hv[n][l]
similaritys = cosine_similarity(t_vector)
print(similaritys)
v = []
for i in range(Num_client):
tmax = -999
for j in range(Num_client):
if i != j and similaritys[i][j] > tmax:
tmax = similaritys[i][j]
