run issue in colab
Opened this issue · 1 comments
ModuleNotFoundError: No module named 'FLPyfhelin'
!wget https://github.com/FebriantiW/Homomorphic-Encryption-and-Federated-Learning-based-Privacy-Preserving-CNN-Training-/blob/main/FLPyfhelin.py
!python FLPyfhelin.py
Traceback (most recent call last):
File "/content/FLPyfhelin.py", line 1, in
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#color_mode='grayscale' "," class_mode='categorical',"," shuffle=True, "," batch_size=BS,"," )"," "," val = image_gen.flow_from_dataframe("," dataframe = df_train,"," directory = train_path,"," x_col='Path', "," y_col='Label',"," subset='validation',"," target_size=image_size,"," #color_mode='grayscale' "," class_mode='categorical',"," shuffle=True, "," batch_size=BS,"," )",""," return train,val","","","","def create_model(load_model_path=None):"," model = Sequential()"," model.add(Conv2D(32, (3, 3), activation="relu", input_shape=input_shape))"," model.add(MaxPooling2D(pool_size = (2, 2)))"," model.add(Conv2D(32, (3, 3), activation="relu", input_shape=input_shape))"," model.add(MaxPooling2D(pool_size = (2, 2)))"," model.add(Conv2D(32, (3, 3), activation="relu", input_shape=input_shape))"," model.add(MaxPooling2D(pool_size = (2, 2)))"," model.add(Conv2D(64, (3, 3), activation="relu", input_shape=input_shape))"," model.add(MaxPooling2D(pool_size = (2, 2)))"," model.add(Conv2D(64, (3, 3), activation="relu", input_shape=input_shape))"," model.add(MaxPooling2D(pool_size = (2, 2)))"," model.add(Conv2D(128, (3, 3), activation="relu", input_shape=input_shape))"," model.add(MaxPooling2D(pool_size = (2, 2)))",""," model.add(Flatten())"," model.add(Dense(activation = 'relu', units = 128))"," model.add(Dense(activation = 'relu', units = 64))"," model.add(Dense(activation = 'softmax', units = 2))"," #cnn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"," "," #model.compile(optimizer = 'Adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])"," opt = Adam(learning_rate=INIT_LR, decay=INIT_LR /10)"," model.compile(loss="categorical_crossentropy", optimizer=opt,metrics=["accuracy"])"," "," if load_model_path:"," model=load_model(load_model_path)",""," return model",""," ","def save_weights(model,ind):"," #names = [weight.name for layer in model.layers for weight in layer.weights]"," weights = np.array(model.get_weights(),dtype='object')"," np.save('weights/weights' + ind+ '.npy', weights, allow_pickle=True)"," return","","def load_weights(ind):"," weights=np.load('weights/weights' + ind + '.npy',allow_pickle=True)"," model=create_model()"," model.set_weights(weights)"," return model","","def train_server(train_ds,val_ds, epoch=10):"," model=create_model()"," "," early = EarlyStopping(monitor="loss", mode="min", patience=3)"," lr_red = ReduceLROnPlateau(monitor="loss", patience = 2, verbose=1,factor=0.3, min_lr=0.000001)",""," checkpoint_path = "weights/main.ckpt""," checkpoint_dir = os.path.dirname(checkpoint_path)"," checkpoint = ModelCheckpoint(filepath=checkpoint_path,save_weights_only=True,save_best_only=True, verbose=1,monitor='accuracy', mode='auto')",""," callbacks_list = [early, lr_red,checkpoint]"," model.fit(train_ds, validation_data=val_ds, epochs=epoch, callbacks=callbacks_list)"," model.load_weights(checkpoint_path)"," save_weights(model,'main')"," model.save('main_model.hdf5')",""," return","","def train_clients(dataframe, train_path, num_clients, epoch=10):"," model=create_model('main_model.hdf5')"," "," #dataframe = prep_df(train_path,shuffle=True):"," "," for i in range(num_clients): "," train_ds, val_ds = get_train_data(dataframe, train_path, i, num_clients)"," early = EarlyStopping(monitor="loss", mode="min", patience=5,restore_best_weights=True)"," lr_red = ReduceLROnPlateau(monitor="loss", patience = 2, verbose=1,factor=0.3, min_lr=0.000001)"," "," checkpoint_path = "weights/client_" + str(i+1)+ ".ckpt""," checkpoint_dir = os.path.dirname(checkpoint_path)"," checkpoint = ModelCheckpoint(filepath=checkpoint_path,save_weights_only=True,save_best_only=True, verbose=1,monitor='accuracy', mode='auto')"," #model.fit(train_ds, validation_data=val_ds, callbacks=[checkpoint], epochs=EPOCHS)"," model.fit(train_ds, validation_data=val_ds, callbacks=[checkpoint,early,lr_red], epochs=epoch)"," #model.load_weights(checkpoint_path)"," "," save_weights(model,str(i+1))"," "," return","","def encrypt_export_weights(indx):"," HE = get_pk()"," model = load_weights(str(indx+1))"," start = time.time()"," encrypted_weights={}"," for i in range(len(model.layers)):"," if model.layers[i].get_weights()!=[]:"," encrypted =[]"," weights = model.layers[i].get_weights() ",""," for j in range(len(weights)):"," weight = weights[j]"," shape = weight.shape"," weight = weight.flatten()"," array= np.empty(len(weight),dtype=PyCtxt)"," "," for k in np.arange(len(weight)):"," array[k] = HE.encryptFrac(weight[k])"," "," enc_array = array.reshape(shape)"," #enc_array = np.array(enc_array,dtype=PyCtxt)"," encrypted_weights['c_'+str(i)+''+str(j)]=enc_array"," "," end = time.time()"," print('Time to encrypt weights:',end-start)"," filename = "weights/client" + str(indx+1)+ ".pickle""," export_weights(filename, encrypted_weights)"," "," return","","def export_weights(filename, encrypted_weights):"," HE = get_pk()"," dic = {}"," dic['key']=HE"," dic['val']=encrypted_weights"," start = time.time()"," with open(filename, 'wb') as handle:"," pickle.dump(dic, handle, protocol=pickle.HIGHEST_PROTOCOL)"," end = time.time()"," print('Time to export weights to pickle:',end-start)"," return","","def export_encrypted_clients_weights(num_client):"," start = time.time()"," for i in range(num_client):"," encrypt_export_weights(i)"," print('Weights exported: Client',i+1)"," end = time.time()"," print('Total time to encrypt and export:',end-start)"," return","","def get_sk():"," filename = "privatekey.pickle""," with open(filename, 'rb') as handle:"," key = pickle.load(handle)"," "," HE = key['HE']"," HE.from_bytes_context(key['con'])"," HE.from_bytes_publicKey(key['pk'])"," HE.from_bytes_secretKey(key['sk'])"," "," return HE","","def decrypt_import_weights(filename):"," start = time.time()"," dec_weights=decrypt_weights(filename)"," end = time.time()"," print('Time to decrypt:',end-start)"," "," model=create_model('main_model.hdf5')"," model.get_weights()"," for i in range(len(model.layers)):"," if model.layers[i].get_weights()!=[]: "," weights = model.layers[i].get_weights()"," weight=[]"," for j in range(len(weights)):"," weight.append(dec_weights['c_'+str(i)+''+str(j)])"," "," model.layers[i].set_weights(weight)"," "," model.save('agg_model.hdf5')"," return model","","def decrypt_weights(filename):"," HE = get_sk()"," enc_weights={}"," dec_weights={}"," enc_weights=import_encrypted_weights(filename)"," "," for key in enc_weights:"," arr = enc_weights[key]"," shape = arr.shape"," weight = arr.flatten()"," "," for l in range(len(weight)):"," weight[l]= HE.decryptFrac(weight[l])",""," dec_array = weight.reshape(shape)"," dec_weights[key] = dec_array",""," return dec_weights","","","def import_encrypted_weights(filename):"," weights={}"," model = create_model()"," start = time.time()"," #filename = "weights/client" + str(indx+1)+ ".pickle""," with open(filename, 'rb') as handle:"," dct = pickle.load(handle)",""," cweights=dct['val']"," HE2 = dct['key']"," enc_weights={}",""," for key in cweights:"," arr = cweights[key]"," shape = arr.shape"," weight = arr.flatten()",""," for l in np.arange(len(weight)):"," weight[l].pyfhel = HE2",""," enc_array = weight.reshape(shape)"," enc_weights[key] = enc_array"," "," end = time.time()"," print('Time to import:',end-start) "," return enc_weights","","def gen_pk(s=128, m=2048):"," HE = Pyfhel() "," HE.contextGen(p=65537, sec = s, m=m)"," HE.keyGen()"," "," keys ={}"," keys['HE'] = HE"," keys['con'] = HE.to_bytes_context()"," keys['pk'] = HE.to_bytes_publicKey()"," "," filename = "publickey.pickle""," with open(filename, 'wb') as handle:"," pickle.dump(keys, handle, protocol=pickle.HIGHEST_PROTOCOL)",""," return HE","","def get_pk():"," filename = "publickey.pickle""," with open(filename, 'rb') as handle:"," key = pickle.load(handle)",""," HE2 = key['HE']"," HE2.from_bytes_context(key['con'])"," HE2.from_bytes_publicKey(key['pk'])",""," return HE2","","def 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