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nd_aggregation.py
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nd_aggregation.py
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import mxnet as mx
from mxnet import nd, autograd, gluon
import numpy as np
from copy import deepcopy
import time
from sklearn.metrics import roc_auc_score
def simple_mean(old_gradients, param_list, net, lr, b=0, hvp=None):
if hvp is not None:
pred_grad = []
distance = []
for i in range(len(old_gradients)):
pred_grad.append(old_gradients[i] + hvp)
#distance.append((1 - nd.dot(pred_grad[i].T, param_list[i]) / (
#nd.norm(pred_grad[i]) * nd.norm(param_list[i]))).asnumpy().item())
pred = np.zeros(100)
pred[:b] = 1
distance = nd.norm((nd.concat(*old_gradients, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
auc1 = roc_auc_score(pred, distance)
distance = nd.norm((nd.concat(*pred_grad, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
auc2 = roc_auc_score(pred, distance)
print("Detection AUC: %0.4f; Detection AUC: %0.4f" % (auc1, auc2))
#distance = nd.norm((nd.concat(*old_gradients, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
#distance = nd.norm(nd.concat(*param_list, dim=1), axis=0).asnumpy()
# normalize distance
distance = distance / np.sum(distance)
else:
distance = None
mean_nd = nd.mean(nd.concat(*param_list, dim=1), axis=-1, keepdims=1)
idx = 0
for j, (param) in enumerate(net.collect_params().values()):
if param.grad_req == 'null':
continue
param.set_data(param.data() - lr * mean_nd[idx:(idx + param.data().size)].reshape(param.data().shape))
idx += param.data().size
return mean_nd, distance
# trimmed mean
def trim(old_gradients, param_list, net, lr, b=0, hvp=None):
'''
gradients: the list of gradients computed by the worker devices
net: the global model
lr: learning rate
byz: attack
f: number of compromised worker devices
b: trim parameter
'''
if hvp is not None:
pred_grad = []
for i in range(len(old_gradients)):
pred_grad.append(old_gradients[i] + hvp)
pred = np.zeros(100)
pred[:b] = 1
distance = nd.norm((nd.concat(*old_gradients, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
auc1 = roc_auc_score(pred, distance)
distance = nd.norm((nd.concat(*pred_grad, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
auc2 = roc_auc_score(pred, distance)
print("Detection AUC: %0.4f; Detection AUC: %0.4f" % (auc1, auc2))
#distance = nd.norm((nd.concat(*old_gradients, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
#distance = nd.norm(nd.concat(*param_list, dim=1), axis=0).asnumpy()
# normalize distance
distance = distance / np.sum(distance)
else:
distance = None
# sort
sorted_array = nd.array(np.sort(nd.concat(*param_list, dim=1).asnumpy(), axis=-1), ctx=mx.gpu(5))
#sorted_array = nd.sort(nd.concat(*param_list, dim=1), axis=-1)
# trim
n = len(param_list)
m = n - b * 2
trim_nd = nd.mean(sorted_array[:, b:(b + m)], axis=-1, keepdims=1)
# update global model
idx = 0
for j, (param) in enumerate(net.collect_params().values()):
if param.grad_req == 'null':
continue
param.set_data(param.data() - lr * trim_nd[idx:(idx + param.data().size)].reshape(param.data().shape))
idx += param.data().size
return trim_nd, distance
def median(old_gradients, param_list, net, lr, b=0, hvp=None):
if hvp is not None:
pred_grad = []
distance = []
for i in range(len(old_gradients)):
pred_grad.append(old_gradients[i] + hvp)
#distance.append((1 - nd.dot(pred_grad[i].T, param_list[i]) / (
#nd.norm(pred_grad[i]) * nd.norm(param_list[i]))).asnumpy().item())
pred = np.zeros(100)
pred[:b] = 1
distance = nd.norm((nd.concat(*old_gradients, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
auc1 = roc_auc_score(pred, distance)
distance = nd.norm((nd.concat(*pred_grad, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
auc2 = roc_auc_score(pred, distance)
print("Detection AUC: %0.4f; Detection AUC: %0.4f" % (auc1, auc2))
#distance = nd.norm((nd.concat(*old_gradients, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
#distance = nd.norm(nd.concat(*param_list, dim=1), axis=0).asnumpy()
# normalize distance
distance = distance / np.sum(distance)
else:
distance = None
if len(param_list) % 2 == 1:
median_nd = nd.concat(*param_list, dim=1).sort(axis=-1)[:, len(param_list) // 2]
else:
median_nd = nd.concat(*param_list, dim=1).sort(axis=-1)[:, len(param_list) // 2: len(param_list) // 2 + 1].mean(axis=-1, keepdims=1)
idx = 0
for j, (param) in enumerate(net.collect_params().values()):
if param.grad_req == 'null':
continue
param.set_data(param.data() - lr * median_nd[idx:(idx + param.data().size)].reshape(param.data().shape))
idx += param.data().size
return median_nd, distance
def score(gradient, v, f):
num_neighbours = v.shape[1] - 2 - f
sorted_distance = nd.square(v - gradient).sum(axis=0).sort()
return nd.sum(sorted_distance[1:(1+num_neighbours)]).asscalar()
def nearest_distance(gradient, c_p):
sorted_distance = nd.square(c_p - gradient).sum(axis=1).sort(axis=0)
return sorted_distance[1].asscalar()
def krum(old_gradients, param_list, net, lr, b=0, hvp=None):
if hvp is not None:
pred_grad = []
distance = []
for i in range(len(old_gradients)):
pred_grad.append(old_gradients[i] + hvp)
#distance.append((1 - nd.dot(pred_grad[i].T, param_list[i]) / (
#nd.norm(pred_grad[i]) * nd.norm(param_list[i]))).asnumpy().item())
pred = np.zeros(100)
pred[:b] = 1
distance = nd.norm((nd.concat(*old_gradients, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
auc1 = roc_auc_score(pred, distance)
distance = nd.norm((nd.concat(*pred_grad, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
auc2 = roc_auc_score(pred, distance)
print("Detection AUC: %0.4f; Detection AUC: %0.4f" % (auc1, auc2))
#distance = nd.norm((nd.concat(*old_gradients, dim=1) - nd.concat(*param_list, dim=1)), axis=0).asnumpy()
#distance = nd.norm(nd.concat(*param_list, dim=1), axis=0).asnumpy()
# normalize distance
distance = distance / np.sum(distance)
else:
distance = None
num_params = len(param_list)
q = b
if num_params <= 2:
# if there are too few clients, randomly pick one as Krum aggregation result
random_idx = np.random.choice(num_params)
krum_nd = nd.reshape(param_list[random_idx], shape=(-1, 1))
else:
if num_params - b - 2 <= 0:
q = num_params-3
v = nd.concat(*param_list, dim=1)
scores = nd.array([score(gradient, v, q) for gradient in param_list])
min_idx = int(scores.argmin(axis=0).asscalar())
krum_nd = nd.reshape(param_list[min_idx], shape=(-1, 1))
idx = 0
for j, (param) in enumerate(net.collect_params().values()):
if param.grad_req == 'null':
continue
param.set_data(param.data() - lr * krum_nd[idx:(idx+param.data().size)].reshape(param.data().shape))
idx += param.data().size
return krum_nd, distance