def compute_encrypted_params_grads(self, X, encrypt_grads):
grads = self.compute_gradients(X)
grads_W = grads[0]
grads_b = grads[1]
encrypt_grads_ex = np.expand_dims(encrypt_grads, axis=1)
encrypt_grads_W = distribute_compute_sum_XY(encrypt_grads_ex, grads_W)
encrypt_grads_b = distribute_compute_sum_XY(encrypt_grads, grads_b)
return encrypt_grads_W, encrypt_grads_b
def _update_loss(self):
uA_overlap_prime = -self.uA_overlap / self.feature_dim
enc_loss_overlap = np.sum(
distribute_compute_sum_XY(uA_overlap_prime, self.enc_uB_overlap))
enc_loss_y = self.__compute_encrypt_loss_y(self.enc_uB_overlap,
self.enc_uB_overlap_2,
self.y_overlap, self.phi)
self.loss = self.alpha * enc_loss_y + enc_loss_overlap
def __compute_encrypt_loss_y(self, enc_uB_overlap, y_overlap, phi):
if self.host_public_key is not None and self.public_key != self.host_public_key:
self.enc_phi_uB_overlap_2_phi = distribute_decrypt_matrix(self.private_key, self.enc_phi_uB_overlap_2_phi)
enc_uB_phi = distribute_encrypt_matmul_2_ob(enc_uB_overlap, phi.transpose())
enc_loss_y = (-0.5 * distribute_compute_sum_XY(y_overlap, enc_uB_phi)[0] + 1.0 / 8 * np.sum(
self.enc_phi_uB_overlap_2_phi)) + len(y_overlap) * np.log(2)
return enc_loss_y
def __compute_encrypt_loss_y(self, enc_uB_overlap, enc_uB_overlap_2,
y_overlap, phi):
enc_uB_phi = distribute_encrypt_matmul_2_ob(enc_uB_overlap,
phi.transpose())
enc_uB_2 = np.sum(enc_uB_overlap_2, axis=0)
enc_phi_uB_2_Phi = distribute_encrypt_matmul_2_ob(
distribute_encrypt_matmul_2_ob(phi, enc_uB_2), phi.transpose())
enc_loss_y = (
-0.5 * distribute_compute_sum_XY(y_overlap, enc_uB_phi)[0] +
1.0 / 8 * np.sum(enc_phi_uB_2_Phi)) + len(y_overlap) * np.log(2)
return enc_loss_y
def test_distributed_calculate_sum_XY(self):
print("--- test_distributed_calculate_sum_XY ---")
X = np.array([[1., 2., 3.],
[4., 5., 6.],
[7., 8., 9.]])
Y = np.array([[1], [-1], [1]])
actual_sum_XY = np.sum(X * Y, axis=0)
sum_XY = distribute_compute_sum_XY(X, Y)
assert_array(actual_sum_XY, sum_XY)
def _update_gradients(self):
# enc_y_overlap_2_phi_uB_overlap_2 was calculated by host
if self.is_trace:
self.logger.debug(
"enc_y_overlap_2_phi_uB_overlap_2 shape" + str(self.enc_y_overlap_2_phi_uB_overlap_2.shape))
if self.host_public_key is not None and self.public_key != self.host_public_key:
# TODO: decrypt enc_y_overlap_2_phi_uB_overlap_2
self.enc_y_overlap_2_phi_uB_overlap_2 = distribute_decrypt_matrix(self.private_key,
self.enc_y_overlap_2_phi_uB_overlap_2)
y_overlap = np.tile(self.y_overlap, (1, self.enc_uB_overlap.shape[-1]))
enc_y_overlap_uB_overlap = distribute_compute_sum_XY(y_overlap * 0.5, self.enc_uB_overlap)
enc_const = np.sum(self.enc_y_overlap_2_phi_uB_overlap_2, axis=0) - enc_y_overlap_uB_overlap
enc_const_overlap = np.tile(enc_const, (len(self.overlap_indexes), 1))
enc_const_nonoverlap = np.tile(enc_const, (len(self.non_overlap_indexes), 1))
y_non_overlap = np.tile(self.y[self.non_overlap_indexes], (1, self.enc_uB_overlap.shape[-1]))
if self.is_trace:
self.logger.debug("enc_const shape:" + str(enc_const.shape))
self.logger.debug("enc_const_overlap shape" + str(enc_const_overlap.shape))
self.logger.debug("enc_const_nonoverlap shape" + str(enc_const_nonoverlap.shape))
self.logger.debug("y_non_overlap shape" + str(y_non_overlap.shape))
enc_grad_A_nonoverlap = distribute_compute_XY(self.alpha * y_non_overlap / len(self.y), enc_const_nonoverlap)
enc_grad_A_overlap = distribute_compute_XY_plus_Z(self.alpha * y_overlap / len(self.y), enc_const_overlap,
self.enc_mapping_comp_B)
if self.is_trace:
self.logger.debug("enc_grad_A_nonoverlap shape" + str(enc_grad_A_nonoverlap.shape))
self.logger.debug("enc_grad_A_overlap shape" + str(enc_grad_A_overlap.shape))
enc_loss_grad_A = [[0 for _ in range(self.enc_uB_overlap.shape[1])] for _ in range(len(self.y))]
for i, j in enumerate(self.non_overlap_indexes):
enc_loss_grad_A[j] = enc_grad_A_nonoverlap[i]
for i, j in enumerate(self.overlap_indexes):
enc_loss_grad_A[j] = enc_grad_A_overlap[i]
enc_loss_grad_A = np.array(enc_loss_grad_A)
if self.is_trace:
self.logger.debug("enc_loss_grad_A shape" + str(enc_loss_grad_A.shape))
self.logger.debug("enc_loss_grad_A" + str(enc_loss_grad_A))
self.loss_grads = enc_loss_grad_A
self.enc_grads_W, self.enc_grads_b = self.localModel.compute_encrypted_params_grads(
self.X, enc_loss_grad_A)
def _update_loss(self):
uA_overlap_prime = -self.uA_overlap / self.feature_dim
enc_loss_overlap = np.sum(
distribute_compute_sum_XY(uA_overlap_prime, self.enc_uB_overlap))
enc_loss_y = self.__compute_encrypt_loss_y(self.enc_uB_overlap,
self.enc_uB_overlap_2,
self.y_overlap, self.phi)
self.loss = self.alpha * enc_loss_y + enc_loss_overlap
print("encloss:===")
print(self.loss)
#New!!!just for testing difference with the plaintext(for loss)
loss_overlap = np.sum(uA_overlap_prime * self.uB_overlap)
loss_y = self.__compute_loss_y(self.uB_overlap, self.y_overlap,
self.phi)
real_loss = self.alpha * loss_y + loss_overlap
print("realloss:===")
print(real_loss)
def _update_gradients(self):
# y_overlap_2 have shape (len(overlap_indexes), 1),
# phi has shape (1, feature_dim),
# y_overlap_2_phi has shape (len(overlap_indexes), 1, feature_dim)
y_overlap_2_phi = np.expand_dims(self.y_overlap_2 * self.phi, axis=1)
# uB_2_overlap has shape (len(overlap_indexes), feature_dim, feature_dim)
enc_y_overlap_2_phi_uB_overlap_2 = distribute_encrypt_matmul_3(
y_overlap_2_phi, self.enc_uB_overlap_2)
enc_loss_grads_const_part1 = np.sum(
0.25 * np.squeeze(enc_y_overlap_2_phi_uB_overlap_2, axis=1),
axis=0)
if self.is_trace:
self.logger.debug("enc_y_overlap_2_phi_uB_overlap_2 shape" +
str(enc_y_overlap_2_phi_uB_overlap_2.shape))
self.logger.debug("enc_loss_grads_const_part1 shape" +
str(enc_loss_grads_const_part1.shape))
y_overlap = np.tile(self.y_overlap, (1, self.enc_uB_overlap.shape[-1]))
enc_loss_grads_const_part2 = distribute_compute_sum_XY(
y_overlap * 0.5, self.enc_uB_overlap)
enc_const = enc_loss_grads_const_part1 - enc_loss_grads_const_part2
enc_const_overlap = np.tile(enc_const, (len(self.overlap_indexes), 1))
enc_const_nonoverlap = np.tile(enc_const,
(len(self.non_overlap_indexes), 1))
y_non_overlap = np.tile(self.y[self.non_overlap_indexes],
(1, self.enc_uB_overlap.shape[-1]))
if self.is_trace:
self.logger.debug("enc_const shape:" + str(enc_const.shape))
self.logger.debug("enc_const_overlap shape" +
str(enc_const_overlap.shape))
self.logger.debug("enc_const_nonoverlap shape" +
str(enc_const_nonoverlap.shape))
self.logger.debug("y_non_overlap shape" + str(y_non_overlap.shape))
enc_grad_A_nonoverlap = distribute_compute_XY(
self.alpha * y_non_overlap / len(self.y), enc_const_nonoverlap)
enc_grad_A_overlap = distribute_compute_XY_plus_Z(
self.alpha * y_overlap / len(self.y), enc_const_overlap,
self.enc_mapping_comp_B)
if self.is_trace:
self.logger.debug("enc_grad_A_nonoverlap shape" +
str(enc_grad_A_nonoverlap.shape))
self.logger.debug("enc_grad_A_overlap shape" +
str(enc_grad_A_overlap.shape))
enc_loss_grad_A = [[0 for _ in range(self.enc_uB_overlap.shape[1])]
for _ in range(len(self.y))]
# TODO: need more efficient way to do following task
for i, j in enumerate(self.non_overlap_indexes):
enc_loss_grad_A[j] = enc_grad_A_nonoverlap[i]
for i, j in enumerate(self.overlap_indexes):
enc_loss_grad_A[j] = enc_grad_A_overlap[i]
enc_loss_grad_A = np.array(enc_loss_grad_A)
if self.is_trace:
self.logger.debug("enc_loss_grad_A shape" +
str(enc_loss_grad_A.shape))
self.logger.debug("enc_loss_grad_A" + str(enc_loss_grad_A))
self.loss_grads = enc_loss_grad_A
self.enc_grads_W, self.enc_grads_b = self.localModel.compute_encrypted_params_grads(
self.X, enc_loss_grad_A)