def create(cls, ftl_model_param: FTLModelParam,
transfer_variable: HeteroFTLTransferVariable, ftl_local_model):
if ftl_model_param.is_encrypt:
if ftl_model_param.enc_ftl == "dct_enc_ftl":
# decentralized encrypted ftl host
LOGGER.debug("@ create decentralized encrypted ftl_host")
host_model = EncryptedFTLHostModel(local_model=ftl_local_model,
model_param=ftl_model_param)
host = HeteroDecentralizedEncryptFTLHost(
host_model, ftl_model_param, transfer_variable)
elif ftl_model_param.enc_ftl == "dct_enc_ftl2":
# decentralized encrypted faster ftl host
LOGGER.debug(
"@ create decentralized encrypted faster ftl_host")
host_model = FasterEncryptedFTLHostModel(
local_model=ftl_local_model, model_param=ftl_model_param)
host = FasterHeteroDecentralizedEncryptFTLHost(
host_model, ftl_model_param, transfer_variable)
elif ftl_model_param.enc_ftl == "enc_ftl2":
# encrypted faster ftl host
LOGGER.debug("@ create encrypted faster ftl_host")
host_model = FasterEncryptedFTLHostModel(
local_model=ftl_local_model, model_param=ftl_model_param)
host = FasterHeteroEncryptFTLHost(host_model, ftl_model_param,
transfer_variable)
#new
elif ftl_model_param.enc_ftl == "enc_ot":
# encrypted OT host
LOGGER.debug("@ create encrypt OT host")
host_model = OTEncryptedFTLHostModel(
local_model=ftl_local_model, model_param=ftl_model_param)
host = HeteroOTEncryptFTLhost(host_model, ftl_model_param,
transfer_variable)
else:
# encrypted ftl host
LOGGER.debug("@ create encrypted ftl_host")
host_model = EncryptedFTLHostModel(local_model=ftl_local_model,
model_param=ftl_model_param)
host = HeteroEncryptFTLHost(host_model, ftl_model_param,
transfer_variable)
else:
# plain ftl host
LOGGER.debug("@ create plain ftl_host")
host_model = PlainFTLHostModel(local_model=ftl_local_model,
model_param=ftl_model_param)
host = HeteroPlainFTLHost(host_model, ftl_model_param,
transfer_variable)
return host
def run_one_party_msg_exchange(autoencoderA,
autoencoderB,
U_A,
U_B,
y,
overlap_indexes,
non_overlap_indexes,
public_key=None,
private_key=None):
fake_model_param = MockFTLModelParam(alpha=1)
partyA = FasterEncryptedFTLGuestModel(autoencoderA,
fake_model_param,
public_key=public_key,
private_key=private_key)
partyA.set_batch(U_A, y, non_overlap_indexes, overlap_indexes)
partyB = FasterEncryptedFTLHostModel(autoencoderB,
fake_model_param,
public_key=public_key,
private_key=private_key)
partyB.set_batch(U_B, overlap_indexes)
[y_overlap_phi, mapping_comp_A, phi, phi_2] = partyA.send_components()
[uB_overlap, mapping_comp_B] = partyB.send_components()
partyA.receive_components([uB_overlap, mapping_comp_B])
partyB.receive_components([y_overlap_phi, mapping_comp_A, phi, phi_2])
precomputed_components_A = partyA.send_precomputed_components()
precomputed_components_B = partyB.send_precomputed_components()
partyA.receive_precomputed_components(precomputed_components_B)
partyB.receive_precomputed_components(precomputed_components_A)
# encrypt_gradients_A = partyA.send_gradients()
# encrypt_gradients_B = partyB.send_gradients()
# partyA.receive_gradients(__decrypt_gradients(encrypt_gradients_A))
# partyB.receive_gradients(__decrypt_gradients(encrypt_gradients_B))
return partyA, partyB
tf.reset_default_graph()
autoencoder_A = Autoencoder(1)
autoencoder_B = Autoencoder(2)
autoencoder_A.build(X_A.shape[-1], 32, learning_rate=0.01)
autoencoder_B.build(X_B.shape[-1], 32, learning_rate=0.01)
paillierEncrypt = PaillierEncrypt()
paillierEncrypt.generate_key()
publickey = paillierEncrypt.get_public_key()
privatekey = paillierEncrypt.get_privacy_key()
fake_model_param = FakeFTLModelParam(alpha=100)
partyA = FasterEncryptedFTLGuestModel(autoencoder_A, fake_model_param, public_key=publickey)
partyB = FasterEncryptedFTLHostModel(autoencoder_B, fake_model_param, public_key=publickey)
federatedLearning = LocalFasterEncryptedFederatedTransferLearning(partyA, partyB, privatekey)
print("################################ Train Federated Models ############################")
start_time = time.time()
epochs = 10
init = tf.global_variables_initializer()
with tf.Session() as sess:
autoencoder_A.set_session(sess)
autoencoder_B.set_session(sess)
sess.run(init)
losses = []
fscores = []
aucs = []