190 KiB
190 KiB
In [1]:
#IMPORTS import numpy as np import random import tensorflow as tf import tensorflow.keras as kr import tensorflow.keras.backend as K from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense from tensorflow.keras.datasets import mnist import os import csv from scipy.spatial.distance import euclidean from sklearn.metrics import confusion_matrix from time import sleep from tqdm import tqdm import copy import numpy from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier import pandas as pd import matplotlib.pyplot as plt import math import seaborn as sns from numpy.random import RandomState import scipy as scp from sklearn.model_selection import train_test_split from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder, LabelEncoder from keras.models import Sequential from keras.layers import Dense from keras import optimizers from keras.callbacks import EarlyStopping,ModelCheckpoint from keras.utils import to_categorical from keras import backend as K from itertools import product from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import roc_auc_score from sklearn.metrics import confusion_matrix from sklearn import mixture from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt %matplotlib inline
Using TensorFlow backend.
In [2]:
# Enter here the data set you want to explain (adult, activity, or synthatic) data_set = 'activity' # Enter here the numb er of peers you want in the experiments n_peers = 100 # Enter here the type of the attack (Byzantine, poisoning, label_flipping) attack_type = 'Byzantine' # the targeted features in case the attack is poisoning attack feature_attacked = [3,5,8] # Enter here the number of attacker peers you want, keep the number of attacker less that 1/2 of the n_peers number_attackers = 3 # enter here the Number of global training epochs, the start and ending epochs of the attacks n_rounds = 10 start_attack_round = 3 end_attack_round = 7 # the threshold for attack detection alpha = 1.2 beta = 1/4
In [3]:
# the random state we will use in the experiments. It can be changed rs = RandomState(92)
In [4]:
# preprocessing adults data set if data_set == 'adult': #Load dataset into a pandas DataFrame adult_data = pd.read_csv('adult_data.csv', na_values='?') # Drop all records with missing values adult_data.dropna(inplace=True) adult_data.reset_index(drop=True, inplace=True) # Drop fnlwgt, not interesting for ML adult_data.drop('fnlwgt', axis=1, inplace=True) adult_data.drop('education', axis=1, inplace=True) # merging some similar features. adult_data['marital-status'].replace('Married-civ-spouse', 'Married', inplace=True) adult_data['marital-status'].replace('Divorced', 'Unmarried', inplace=True) adult_data['marital-status'].replace('Never-married', 'Unmarried', inplace=True) adult_data['marital-status'].replace('Separated', 'Unmarried', inplace=True) adult_data['marital-status'].replace('Widowed', 'Unmarried', inplace=True) adult_data['marital-status'].replace('Married-spouse-absent', 'Married', inplace=True) adult_data['marital-status'].replace('Married-AF-spouse', 'Married', inplace=True) adult_data = pd.concat([adult_data,pd.get_dummies(adult_data['income'], prefix='income')],axis=1) adult_data.drop('income', axis=1, inplace=True) obj_columns = adult_data.select_dtypes(['object']).columns adult_data[obj_columns] = adult_data[obj_columns].astype('category') # Convert numerics to floats and normalize num_columns = adult_data.select_dtypes(['int64']).columns adult_data[num_columns] = adult_data[num_columns].astype('float64') for c in num_columns: #adult[c] -= adult[c].mean() #adult[c] /= adult[c].std() adult_data[c] = (adult_data[c] - adult_data[c].min()) / (adult_data[c].max()-adult_data[c].min()) # 'workclass', 'marital-status', 'occupation', 'relationship' ,'race', 'gender', 'native-country' # adult_data['income'] = adult_data['income'].cat.codes adult_data['marital-status'] = adult_data['marital-status'].cat.codes adult_data['occupation'] = adult_data['occupation'].cat.codes adult_data['relationship'] = adult_data['relationship'].cat.codes adult_data['race'] = adult_data['race'].cat.codes adult_data['gender'] = adult_data['gender'].cat.codes adult_data['native-country'] = adult_data['native-country'].cat.codes adult_data['workclass'] = adult_data['workclass'].cat.codes num_columns = adult_data.select_dtypes(['int8']).columns adult_data[num_columns] = adult_data[num_columns].astype('float64') for c in num_columns: #adult[c] -= adult[c].mean() #adult[c] /= adult[c].std() adult_data[c] = (adult_data[c] - adult_data[c].min()) / (adult_data[c].max()-adult_data[c].min()) display(adult_data.info()) display(adult_data.head(10)) adult_data = adult_data.to_numpy() # splite the data to train and test datasets X_train, X_test, y_train, y_test = train_test_split(adult_data[:,:-2],adult_data[:,-2:], test_size=0.07, random_state=rs) # the names of the features names = ['age','workclass','educational-num','marital-status','occupation', 'relationship','race','gender','capital-gain','capital-loss','hours-per-week','native-country'] Features_number = len(X_train[0])
In [5]:
if data_set == 'synthatic': #generate the data X, y = make_classification(n_samples=1000000, n_features=10, n_redundant=3, n_repeated=2, #n_classes=3, n_informative=5, n_clusters_per_class=4, random_state=42) y = pd.DataFrame(data=y, columns=["y"]) y = pd.get_dummies(y['y'], prefix='y') y = y.to_numpy() X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.07, random_state=rs) # the names of the features names = ['X(0)','X(1)','X(2)','X(3)','X(4)','X(5)','X(6)','X(7)','X(8)','X(9)'] Features_number = len(X_train[0])
In [6]:
if data_set == 'activity': #Load dataset into a pandas DataFrame activity = pd.read_csv("activity_3_original.csv", sep=',') # drop some features that have non value in the majority of the samples to_drop = ['subject', 'timestamp', 'heart_rate','activityID'] activity.drop(axis=1, columns=to_drop, inplace=True) # prepare the truth activity = pd.concat([activity,pd.get_dummies(activity['motion'], prefix='motion')],axis=1) activity.drop('motion', axis=1, inplace=True) class_label = [ 'motion_n', 'motion_y'] predictors = [a for a in activity.columns.values if a not in class_label] for p in predictors: activity[p].fillna(activity[p].mean(), inplace=True) display(predictors) for p in predictors: activity[p] = (activity[p]-activity[p].min()) / (activity[p].max() - activity[p].min()) activity[p].astype('float32') activity = activity.to_numpy() X_train, X_test, y_train, y_test = train_test_split(activity[:,:-2],activity[:,-2:], test_size=0.07, random_state=rs) # the names of the features names = ['temp_hand','acceleration_16_x_hand', 'acceleration_16_y_hand','acceleration_16_z_hand','acceleration_6_x_hand', 'acceleration_6_y_hand','acceleration_6_z_hand','gyroscope_x_hand','gyroscope_y_hand', 'gyroscope_z_hand','magnetometer_x_hand','magnetometer_y_hand','magnetometer_z_hand', 'temp_chest','acceleration_16_x_chest','acceleration_16_y_chest','acceleration_16_z_chest','acceleration_6_x_chest', 'acceleration_6_y_chest','acceleration_6_z_chest','gyroscope_x_chest','gyroscope_y_chest','gyroscope_z_chest', 'magnetometer_x_chest','magnetometer_y_chest','magnetometer_z_chest','temp_ankle','acceleration_16_x_ankle', 'acceleration_16_y_ankle','acceleration_16_z_ankle','acceleration_6_x_ankle','acceleration_6_y_ankle', 'acceleration_6_z_ankle','gyroscope_x_ankle','gyroscope_y_ankle','gyroscope_z_ankle','magnetometer_x_ankle', 'magnetometer_y_ankle','magnetometer_z_ankle'] Features_number = len(X_train[0])
['temp_hand', 'acceleration_16_x_hand', 'acceleration_16_y_hand', 'acceleration_16_z_hand', 'acceleration_6_x_hand', 'acceleration_6_y_hand', 'acceleration_6_z_hand', 'gyroscope_x_hand', 'gyroscope_y_hand', 'gyroscope_z_hand', 'magnetometer_x_hand', 'magnetometer_y_hand', 'magnetometer_z_hand', 'temp_chest', 'acceleration_16_x_chest', 'acceleration_16_y_chest', 'acceleration_16_z_chest', 'acceleration_6_x_chest', 'acceleration_6_y_chest', 'acceleration_6_z_chest', 'gyroscope_x_chest', 'gyroscope_y_chest', 'gyroscope_z_chest', 'magnetometer_x_chest', 'magnetometer_y_chest', 'magnetometer_z_chest', 'temp_ankle', 'acceleration_16_x_ankle', 'acceleration_16_y_ankle', 'acceleration_16_z_ankle', 'acceleration_6_x_ankle', 'acceleration_6_y_ankle', 'acceleration_6_z_ankle', 'gyroscope_x_ankle', 'gyroscope_y_ankle', 'gyroscope_z_ankle', 'magnetometer_x_ankle', 'magnetometer_y_ankle', 'magnetometer_z_ankle']
In [7]:
#begin federated earlystopping = EarlyStopping(monitor = 'val_loss', min_delta = 0.01, patience = 50, verbose = 0, baseline = 2, restore_best_weights = True) checkpoint = ModelCheckpoint('test.h8', monitor='val_loss', mode='min', save_best_only=True, verbose=0) model = Sequential() model.add(Dense(70, input_dim=Features_number, activation='relu')) model.add(Dense(50, activation='relu')) model.add(Dense(50, activation='relu')) model.add(Dense(2, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy']) history = model.fit(X_train, y_train, epochs=2, validation_data=(X_test, y_test), callbacks = [checkpoint, earlystopping], shuffle=True)
Train on 1806870 samples, validate on 136002 samples Epoch 1/2
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In [8]:
#AUXILIARY METHODS FOR FEDERATED LEARNING # RETURN INDICES TO LAYERS WITH WEIGHTS AND BIASES def trainable_layers(model): return [i for i, layer in enumerate(model.layers) if len(layer.get_weights()) > 0] # RETURN WEIGHTS AND BIASES OF A MODEL def get_parameters(model): weights = [] biases = [] index = trainable_layers(model) for i in index: weights.append(copy.deepcopy(model.layers[i].get_weights()[0])) biases.append(copy.deepcopy(model.layers[i].get_weights()[1])) return weights, biases # SET WEIGHTS AND BIASES OF A MODEL def set_parameters(model, weights, biases): index = trainable_layers(model) for i, j in enumerate(index): model.layers[j].set_weights([weights[i], biases[i]]) # DEPRECATED: RETURN THE GRADIENTS OF THE MODEL AFTER AN UPDATE def get_gradients(model, inputs, outputs): """ Gets gradient of model for given inputs and outputs for all weights""" grads = model.optimizer.get_gradients(model.total_loss, model.trainable_weights) symb_inputs = (model._feed_inputs + model._feed_targets + model._feed_sample_weights) f = K.function(symb_inputs, grads) x, y, sample_weight = model._standardize_user_data(inputs, outputs) output_grad = f(x + y + sample_weight) w_grad = [w for i,w in enumerate(output_grad) if i%2==0] b_grad = [w for i,w in enumerate(output_grad) if i%2==1] return w_grad, b_grad # RETURN THE DIFFERENCE OF MODELS' WEIGHTS AND BIASES AFTER AN UPDATE # NOTE: LEARNING RATE IS APPLIED, SO THE UPDATE IS DIFFERENT FROM THE # GRADIENTS. IN CASE VANILLA SGD IS USED, THE GRADIENTS ARE OBTAINED # AS (UPDATES / LEARNING_RATE) def get_updates(model, inputs, outputs, batch_size, epochs): w, b = get_parameters(model) #model.train_on_batch(inputs, outputs) model.fit(inputs, outputs, batch_size=batch_size, epochs=epochs, verbose=0) w_new, b_new = get_parameters(model) weight_updates = [old - new for old,new in zip(w, w_new)] bias_updates = [old - new for old,new in zip(b, b_new)] return weight_updates, bias_updates # UPDATE THE MODEL'S WEIGHTS AND PARAMETERS WITH AN UPDATE def apply_updates(model, eta, w_new, b_new): w, b = get_parameters(model) new_weights = [theta - eta*delta for theta,delta in zip(w, w_new)] new_biases = [theta - eta*delta for theta,delta in zip(b, b_new)] set_parameters(model, new_weights, new_biases) # FEDERATED AGGREGATION FUNCTION def aggregate(n_layers, n_peers, f, w_updates, b_updates): agg_w = [f([w_updates[j][i] for j in range(n_peers)], axis=0) for i in range(n_layers)] agg_b = [f([b_updates[j][i] for j in range(n_peers)], axis=0) for i in range(n_layers)] return agg_w, agg_b # SOLVE NANS def nans_to_zero(W, B): W0 = [np.nan_to_num(w, nan=0.0, posinf=0.0, neginf=0.0) for w in W] B0 = [np.nan_to_num(b, nan=0.0, posinf=0.0, neginf=0.0) for b in B] return W0, B0 def build_forest(X,y): clf=RandomForestClassifier(n_estimators=1000, max_depth=7, random_state=0, verbose = 1) clf.fit(X,y) return clf # COMPUTE EUCLIDEAN DISTANCE OF WEIGHTS def dist_weights(w_a, w_b): wf_a = flatten_weights(w_a) wf_b = flatten_weights(w_b) return euclidean(wf_a, wf_b) # TRANSFORM ALL WEIGHT TENSORS TO 1D ARRAY def flatten_weights(w_in): h = w_in[0].reshape(-1) for w in w_in[1:]: h = np.append(h, w.reshape(-1)) return h
In [9]:
def byzantine_attack_data(inputs): attack_persentage = 40 number_of_attacked_samples = len(inputs) * attack_persentage /100 number_of_attacked_samples = int(number_of_attacked_samples) sampels_attacked = random.sample(range(len(inputs)), number_of_attacked_samples) if data_set == 'adult': z=0 C=0 z=inputs.max(axis = 0) C=inputs.min(axis = 0) for i in range(len(inputs)): if i in sampels_attacked: for j in range(len(inputs[0])): inputs[i][j]= random.uniform(z[j], C[j]) return inputs
In [10]:
def poisoning_attack_data(h, feature_attacked): attack_persentage = 60 number_of_attacked_samples = len(h) * attack_persentage /100 number_of_attacked_samples = int(number_of_attacked_samples) sampels_attacked = random.sample(range(len(h)), number_of_attacked_samples) if data_set == 'adult': z=0 C=0 z=h.max(axis = 0) C=h.min(axis = 0) for i in range(len(h)): if i in sampels_attacked: for j in range(len(feature_attacked)): h[i][feature_attacked[j]]= random.uniform(z[feature_attacked[j]], C[feature_attacked[j]]) return h
In [11]:
def label_flipping_attack_data(z): attack_persentage = 50 number_of_attacked_samples = len(z) * attack_persentage /100 number_of_attacked_samples = int(number_of_attacked_samples) sampels_attacked = random.sample(range(len(z)), number_of_attacked_samples) if data_set == 'adult': for i in range(len(z)): if i in sampels_attacked: for j in range(len(z[i])): if z[i][j] == 0: z[i][j] = 1 else: z[i][j] = 0 return z
In [12]:
# scan the forest for trees maches the wrong predictions of the black-box def scan_wrong(forest_predictions, FL_predict1, forest , y_test_local, X_test_local): sum_feature_improtance= 0 overal_wrong_feature_importance = 0 counter = 0 second_counter = 0 never_seen = 0 avr_wrong_importance = 0 FL_predict1 = np.argmax(FL_predict1, axis=1) forest_predictions = np.argmax(forest_predictions, axis=1) y_test_local = np.argmax(y_test_local, axis=1) FL_wrong = 0 for i in range (len(FL_predict1)): i_tree = 0 # if the black-box got a wrong prediction if (FL_predict1[i] != y_test_local[i]): FL_wrong = FL_wrong + 1 # getting the prediction of the trees one by one for tree_in_forest in forest.estimators_: sample = X_test_local[i].reshape(1, -1) temp = forest.estimators_[i_tree].predict(sample) temp = np.argmax(temp, axis=1) # print('the prediction of the t') # print(temp) i_tree = i_tree + 1 # if the prediction of the tree maches the predictions of the black-box if(FL_predict1[i] == temp): # getting the features importances sum_feature_improtance = sum_feature_improtance + tree_in_forest.feature_importances_ counter = counter + 1 # if we have trees maches the black-box predictions if(counter>0): ave_feature_importence = sum_feature_improtance/counter overal_wrong_feature_importance = ave_feature_importence + overal_wrong_feature_importance second_counter = second_counter + 1 counter = 0 sum_feature_improtance = 0 # if there is no trees maches the black-box predictions else: if(FL_predict1[i] != y_test_local[i]): never_seen = never_seen +1 # getting the average features importances for all the samples that had wrong predictions. if(second_counter>0): avr_wrong_importance = overal_wrong_feature_importance / second_counter return avr_wrong_importance
In [13]:
trainable_layers(model)
Out[13]:
[0, 1, 2, 3]
In [14]:
get_parameters(model)
Out[14]:
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In [15]:
get_updates(model, X_train, y_train, 32, 2)
Out[15]:
([array([[ 2.8014183e-06, 0.0000000e+00, -4.5895576e-06, ..., -2.2205949e-02, 0.0000000e+00, 0.0000000e+00], [ 2.1923333e-06, 0.0000000e+00, -7.8976154e-06, ..., 4.9105823e-02, 0.0000000e+00, 0.0000000e+00], [ 1.5608966e-06, 0.0000000e+00, -3.7252903e-06, ..., 7.3180795e-03, 0.0000000e+00, 0.0000000e+00], ..., [ 1.5050173e-06, 0.0000000e+00, -7.4207783e-06, ..., -2.6787940e-01, 0.0000000e+00, 0.0000000e+00], [ 1.8626451e-06, 0.0000000e+00, -8.1695616e-06, ..., 1.6834244e-02, 0.0000000e+00, 0.0000000e+00], [ 1.7285347e-06, 0.0000000e+00, -4.6193600e-06, ..., -5.3731605e-02, 0.0000000e+00, 0.0000000e+00]], dtype=float32), array([[ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., -4.1723251e-07, -1.3783574e-07, 0.0000000e+00], [ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00], [ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00], ..., [-8.3480060e-02, -7.9958737e-03, 0.0000000e+00, ..., 1.0635569e-01, -3.4096837e-04, 6.9890119e-02], [ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00], [ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00, 0.0000000e+00, 0.0000000e+00]], dtype=float32), array([[-0.04616958, 0.59762466, -0.26143235, ..., -0.06850739, -0.4217179 , 0.08980078], [-0.17200631, 0.3219571 , 0.27451754, ..., 0.07618259, 0.48217025, 0.26553166], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ], ..., [ 0.37040782, 0.77656084, -0.17277646, ..., -0.04284477, 0.27722144, -0.29050782], [ 0.49921095, -0.3744073 , -0.79761034, ..., 0.06160343, 0.26952648, 0.44993538], [ 0.12304485, 0.08797711, 0.14218739, ..., 0.40071172, 0.43845785, 0.22023249]], dtype=float32), array([[ 0.01556432, -0.01553738], [-0.80137116, 0.8013473 ], [ 0.05863538, -0.05863395], [ 0.09222463, -0.09223729], [ 0.22395234, -0.22392958], [-0.25126195, 0.25127405], [ 0.19329323, -0.1932973 ], [-0.42185718, 0.4218619 ], [-0.09126899, 0.09127331], [-0.13490325, 0.13491231], [ 0.07229513, -0.07227075], [-0.1858536 , 0.18585992], [ 0.3444129 , -0.3444115 ], [-0.04673225, 0.04675576], [-0.744485 , 0.7444947 ], [ 0.59019566, -0.5901569 ], [ 0.00766785, -0.0076676 ], [ 0.1348064 , -0.13479984], [ 0.19738197, -0.19736266], [ 0.20221901, -0.20221877], [ 0.2895143 , -0.28952658], [ 0.20282978, -0.20283176], [-0.76195276, 0.7619654 ], [ 0.26977128, -0.2697831 ], [ 0.3250053 , -0.32500228], [-0.15308547, 0.15309238], [-0.10742784, 0.10744089], [-0.15101504, 0.15102363], [-0.6646027 , 0.6646162 ], [ 0.23995936, -0.23995501], [-0.30166405, 0.30165863], [ 0.4730208 , -0.47301006], [ 0. , 0. ], [-0.02773407, 0.02774608], [ 0.07481575, -0.07481062], [-0.51466 , 0.51466644], [-0.02005684, 0.02005637], [-0.11586785, 0.11588269], [ 0.16156894, -0.16156316], [-0.30240393, 0.3024137 ], [ 0.8963616 , -0.89635366], [-0.06791203, 0.06791377], [-0.17452781, 0.17454016], [ 0.08860135, -0.08860841], [-0.6942698 , 0.69428754], [-0.17503893, 0.17506146], [ 0.16636667, -0.16637546], [-0.25057983, 0.25059932], [ 0.2041834 , -0.20417154], [-0.01968396, 0.01968992]], dtype=float32)], [array([ 3.3285469e-06, 0.0000000e+00, -1.2369826e-05, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 8.8644736e-03, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 6.6848241e-02, 0.0000000e+00, 0.0000000e+00, -2.9313583e-03, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, -9.5326707e-02, -3.0721266e-02, 8.1290156e-03, -6.8495750e-02, 0.0000000e+00, 0.0000000e+00, 1.8126052e-02, 0.0000000e+00, 0.0000000e+00, -6.3957557e-02, -4.2761512e-02, -4.0137991e-03, 2.5934458e-02, 0.0000000e+00, 0.0000000e+00, 4.7971878e-02, 8.1122592e-02, -6.5605357e-02, 0.0000000e+00, 0.0000000e+00, -1.6420111e-03, 5.2435798e-09, 0.0000000e+00, 0.0000000e+00, 4.5959245e-02, -1.8143710e-02, 9.4908625e-03, 4.1580014e-04, 0.0000000e+00, -6.7653313e-02, 0.0000000e+00, 0.0000000e+00, -1.2950024e-01, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, -1.7437223e-02, 0.0000000e+00, -2.3985766e-03, -8.6141318e-02, 0.0000000e+00, 1.0811789e-01, 0.0000000e+00, 0.0000000e+00], dtype=float32), array([-0.00627401, -0.191559 , 0. , 0.00838837, 0.0529352 , 0.12782945, 0. , 0.0156331 , 0. , 0. , -0.11151084, 0.0588697 , -0.02708764, 0.07176588, -0.02906452, 0.11350146, 0. , 0.06241233, 0.14389527, 0.15049464, 0.1770727 , -0.03121255, 0.02478044, -0.2777393 , -0.09406517, 0.12997295, 0.13041441, -0.1484769 , 0.2955907 , 0.00798142, 0. , 0. , 0.0003342 , 0.05636339, 0. , 0.06773217, 0.04719278, 0.12417503, -0.039441 , 0.14855272, 0.03889409, 0.10604867, -0.0547139 , -0.06550588, 0.14823543, 0.01506494, 0.08808593, 0.1861152 , 0.01942448, 0.11284278], dtype=float32), array([ 2.54564315e-01, 1.08867824e-01, 5.61506003e-02, -1.75794929e-01, 2.33219430e-01, -1.58185065e-02, -1.31240949e-01, 8.01615715e-02, 2.55392641e-02, 3.14171731e-01, -1.29695922e-01, -5.60564399e-02, -3.62583399e-02, 3.38414431e-01, 2.22075596e-01, 1.01215661e-01, 2.62457654e-02, -2.69618690e-01, 1.52954599e-02, -1.20112836e-01, 2.02051371e-01, -7.11961389e-02, -3.22458982e-01, 2.04761773e-01, 9.38781500e-02, 2.39238501e-01, -9.64630842e-02, 2.50120312e-01, -3.17509770e-02, 2.22122446e-01, 3.26190665e-02, 4.22861874e-02, -2.37673521e-06, 4.52675968e-02, 2.75943756e-01, 1.95241719e-01, -2.34854549e-01, -7.68669993e-02, -7.01770782e-02, 2.47255087e-01, 2.31929541e-01, 1.45128936e-01, 4.07781303e-01, -2.12165058e-01, 3.37380767e-02, -2.01950014e-01, -1.65518045e-01, -4.14324701e-02, 4.91949677e-01, 2.43567675e-01], dtype=float32), array([-0.139424 , 0.13942933], dtype=float32)])
In [16]:
W = get_parameters(model)[0] B = get_parameters(model)[1]
In [17]:
# BASELINE SCENARIO #buid the model as base line for the shards (sequential) # Number of peers #accordin to what we need ss = int(len(X_train)/n_peers) inputs_in = X_train[0*ss:0*ss+ss] outputs_in = y_train[0*ss:0*ss+ss] def build_model(X_t, y_t): model = Sequential() model.add(Dense(70, input_dim=Features_number, activation='relu')) model.add(Dense(50, activation='relu')) model.add(Dense(50, activation='relu')) model.add(Dense(2, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy']) model.fit(X_t, y_t, batch_size=32, epochs=250, verbose=0, validation_data=((X_test, y_test))) return model
In [18]:
display(model.summary())
Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 70) 2800 _________________________________________________________________ dense_2 (Dense) (None, 50) 3550 _________________________________________________________________ dense_3 (Dense) (None, 50) 2550 _________________________________________________________________ dense_4 (Dense) (None, 2) 102 ================================================================= Total params: 9,002 Trainable params: 9,002 Non-trainable params: 0 _________________________________________________________________
None
In [19]:
# predict probabilities for test set yhat_probs = model.predict(X_test, verbose=0) # predict crisp classes for test set yhat_classes = model.predict_classes(X_test, verbose=0)
In [20]:
# accuracy: (tp + tn) / (p + n) accuracy = accuracy_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('Accuracy: %f' % accuracy) # precision tp / (tp + fp) precision = precision_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('Precision: %f' % precision) # recall: tp / (tp + fn) recall = recall_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('Recall: %f' % recall) # f1: 2 tp / (2 tp + fp + fn) f1 = f1_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('F1 score: %f' % f1)
Accuracy: 0.997338 Precision: 0.996095 Recall: 0.999387 F1 score: 0.997738
In [21]:
# confusion matrix mat = confusion_matrix(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) display(mat) plt.matshow(mat); plt.colorbar() plt.show()
array([[55804, 313], [ 49, 79836]], dtype=int64)
In [22]:
# the dectinary FI_dic1= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[]} ave_FI_dic= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[]} targeted_Features ={0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[]} rounds_attack_detected ={0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[]}
In [ ]:
# select aa random peer to be the scanner peer peers_selected = random.sample(range(n_peers), number_attackers+1) scaner = peers_selected[0] mal = peers_selected[1 :] if scaner == 0: scaner = random.sample(range(n_peers), 1) # Percentage and number of peers participating at each global training epoch percentage_participants = 1.0 n_participants = int(n_peers * percentage_participants) # the feature you want to attack in case of a poisoning attack feature_attacked = [3,5,8] # Number of local training epochs per global training epoch n_local_rounds = 5 # Local batch size local_batch_size = 32 # Local learning rate local_lr = 0.001 # Global learning rate or 'gain' model_substitution_rate = 1.0 # Attack detection / prevention mechanism = {None, 'distance', 'median', 'accuracy', 'krum'} discard_outliers = None # Used in 'dist' attack detection, defines how far the outliers are (1.5 is a typical value) tau = 1.5 # Used in 'accuracy' attack detection, defines the error margin for the accuracy improvement sensitivity = 0.05 # Used in 'krum' attack detection, defines how many byzantine attackers we want to defend against tolerance=4 # Prevent suspicious peers from participating again, only valid for 'dist' and 'accuracy' ban_malicious = False # Clear nans and infinites in model updates clear_nans = True number_for_threshold1 = numpy.empty(20, dtype=float) number_for_threshold2 = numpy.empty(20, dtype=float) for r in range(len(number_for_threshold1)): number_for_threshold1[r] = 0 number_for_threshold2[r] = 0 ######################## # ATTACK CONFIGURATION # ######################## # Percentage of malicious peers r_malicious_peers = 0.0 # Number of malicious peers (absolute or relative to total number of peers) n_malicious_peers = int(n_peers * r_malicious_peers) #n_malicious_peers = 1 # Malicious peers malicious_peer = range(n_malicious_peers) # Target for coalitions common_attack_target = [4,7] # Target class of the attack, per each malicious peer malicious_targets = dict([(p, t) for p,t in zip(malicious_peer, [common_attack_target]*n_malicious_peers)]) # Boosting parameter per each malicious peer common_malicious_boost = 12 malicious_boost = dict([(p, b) for p,b in zip(malicious_peer, [common_malicious_boost]*n_malicious_peers)]) ########### # METRICS # ########### metrics = {'accuracy': [], 'atk_effectivity': [], 'update_distances': [], 'outliers_detected': [], 'acc_no_target': []} #################################### # MODEL AND NETWORK INITIALIZATION # #################################### inputs = X_train[0*ss:0*ss+ss] outputs = y_train[0*ss:0*ss+ss] global_model = build_model(inputs,outputs) n_layers = len(trainable_layers(global_model)) print('Initializing network.') sleep(1) network = [] for i in tqdm(range(n_peers)): ss = int(len(X_train)/n_peers) inputs = X_train[i*ss:i*ss+ss] outputs = y_train[i*ss:i*ss+ss] # network.append(build_model(inputs, outputs)) network.append(global_model) banned_peers = set() ################## # BEGIN TRAINING # ################## for t in range(n_rounds): print(f'Round {t+1}.') sleep(1) ## SERVER SIDE ################################################################# # Fetch global model parameters global_weights, global_biases = get_parameters(global_model) if clear_nans: global_weights, global_biases = nans_to_zero(global_weights, global_biases) # Initialize peer update lists network_weight_updates = [] network_bias_updates = [] # Selection of participant peers in this global training epoch if ban_malicious: good_peers = list([p for i,p in enumerate(network) if i not in banned_peers]) n_participants = n_participants if n_participants <= len(good_peers) else int(len(good_peers) * percentage_participants) participants = random.sample(list(enumerate(good_peers)), n_participants) else: participants = random.sample(list(enumerate(network)),n_participants) ################################################################################ ## CLIENT SIDE ################################################################# for i, local_model in tqdm(participants): # Update local model with global parameters set_parameters(local_model, global_weights, global_biases) # Initialization of user data ss = int(len(X_train)/n_peers) inputs = X_train[i*ss:i*ss+ss] outputs = y_train[i*ss:i*ss+ss] # the scanner peer side if(i == scaner): X_train_local, X_test_local, y_train_local, y_test_local = train_test_split(inputs,outputs, test_size=0.7, random_state=rs) inputs = X_train_local outputs = y_train_local if(t == 0): forest = build_forest(X_train_local,y_train_local) forest_predictions = forest.predict(X_test_local) acc_forest = np.mean([t==p for t,p in zip(y_test_local, forest_predictions)]) FL_predict1 = global_model.predict(X_test_local) imp = scan_wrong(forest_predictions, FL_predict1, forest , y_test_local, X_test_local) FI_dic1[t] = imp if t > 0: ave_FI_dic[t-1] = abs(FI_dic1[t] - FI_dic1[t-1]) average_overall_changes = 0 if t > 1: for r in range(0,t): average_overall_changes = average_overall_changes + ave_FI_dic[r] average_overall_changes = average_overall_changes / t total_Changes=0 average_overall_changes_one_val = 0 for s in range(len(ave_FI_dic[t-1])): total_Changes = total_Changes + ave_FI_dic[t-1][s] average_overall_changes_one_val = average_overall_changes_one_val + average_overall_changes[s] print('total_Changes in this round: ', total_Changes) print('average changes: ' ,average_overall_changes_one_val) threshold1 = alpha * average_overall_changes_one_val print(threshold1) if total_Changes >= threshold1: for ra in range(len(ave_FI_dic[t-1])): rounds_attack_detected[t+1] = 1 if ave_FI_dic[t-1][ra] > beta * total_Changes: print('attack have been detected') targeted_Features[t+1] =names[ra] print("attack detected on feature ", names[ra]) # atttacker peer side if(t+1>=start_attack_round and t+1<=end_attack_round): if (i in mal): print("I am peer ",i,"I started the attack, at round", t+1) #attack if attack_type == 'Byzantine': inputs = byzantine_attack_data(inputs) elif attack_type == 'poisoning': inputs = poisoning_attack_data(inputs, feature_attacked) elif attack_type == 'label_flipping': outputs = label_flipping_attack_data(outputs) local_weight_updates, local_bias_updates = get_updates(local_model, inputs, outputs, local_batch_size, n_local_rounds) if clear_nans: local_weight_updates, local_bias_updates = nans_to_zero(local_weight_updates, local_bias_updates) else: # Benign peer # Train local model local_weight_updates, local_bias_updates = get_updates(local_model, inputs, outputs, local_batch_size, n_local_rounds) if clear_nans: local_weight_updates, local_bias_updates = nans_to_zero(local_weight_updates, local_bias_updates) # Send updates to the server network_weight_updates.append(local_weight_updates) network_bias_updates.append(local_bias_updates) ## END OF CLIENT SIDE ########################################################## ###################################### # SERVER SIDE AGGREGATION MECHANISMS # ###################################### # Aggregate client updates aggregated_weights, aggregated_biases = aggregate(n_layers, n_participants, np.mean, network_weight_updates, network_bias_updates) if clear_nans: aggregated_weights, aggregated_biases = nans_to_zero(aggregated_weights, aggregated_biases) # Apply updates to global model apply_updates(global_model, model_substitution_rate, aggregated_weights, aggregated_biases) # Proceed as in first case aggregated_weights, aggregated_biases = aggregate(n_layers, n_participants, np.mean, network_weight_updates, network_bias_updates) if clear_nans: aggregated_weights, aggregated_biases = nans_to_zero(aggregated_weights, aggregated_biases) apply_updates(global_model, model_substitution_rate, aggregated_weights, aggregated_biases) ################### # COMPUTE METRICS # ################### # Global model accuracy score = global_model.evaluate(X_test, y_test, verbose=0) print(f'Global model loss: {score[0]}; global model accuracy: {score[1]}') metrics['accuracy'].append(score[1]) # Accuracy without the target score = global_model.evaluate(X_test, y_test, verbose=0) metrics['acc_no_target'].append(score[1]) # Distance of individual updates to the final aggregation metrics['update_distances'].append([dist_weights(aggregated_weights, w_i) for w_i in network_weight_updates])
Initializing network.
100%|████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 100342.20it/s]
Round 1.
57%|██████████████████████████████████████████████▏ | 57/100 [02:21<01:44, 2.44s/it][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 12.6s finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.4s finished 100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [04:35<00:00, 2.75s/it]
Global model loss: 0.4784254086968245; global model accuracy: 0.8537300825119019 Round 2.
32%|█████████████████████████▉ | 32/100 [01:19<02:44, 2.42s/it][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.5s finished 100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [06:19<00:00, 3.79s/it]
Global model loss: 1.913670726969011; global model accuracy: 0.7795841097831726 Round 3.
0%| | 0/100 [00:00<?, ?it/s]
I am peer 58 I started the attack, at round 3
9%|███████▍ | 9/100 [00:02<00:25, 3.57it/s]
I am peer 51 I started the attack, at round 3
23%|██████████████████▋ | 23/100 [00:05<00:19, 4.01it/s][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.4s finished
total_Changes in this round: 0.05399442952366329 average changes: 0.058936568090674844 0.0707238817088098
35%|████████████████████████████▎ | 35/100 [03:17<05:24, 4.99s/it]
I am peer 95 I started the attack, at round 3
100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [03:20<00:00, 2.00s/it]
Global model loss: 1.9800631387364644; global model accuracy: 0.6950191855430603 Round 4.
0%| | 0/100 [00:00<?, ?it/s]
I am peer 58 I started the attack, at round 4
8%|██████▌ | 8/100 [00:02<00:29, 3.17it/s]
I am peer 95 I started the attack, at round 4
42%|██████████████████████████████████ | 42/100 [00:05<00:14, 4.09it/s][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.4s finished
total_Changes in this round: 0.07331074788490739 average changes: 0.06372796135541901 0.07647355362650281
57%|██████████████████████████████████████████████▏ | 57/100 [04:30<03:55, 5.48s/it]
I am peer 51 I started the attack, at round 4
100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [04:33<00:00, 2.73s/it]
Global model loss: 0.1299796798054893; global model accuracy: 0.9635814428329468 Round 5.
0%| | 0/100 [00:00<?, ?it/s]
I am peer 51 I started the attack, at round 5
24%|███████████████████▍ | 24/100 [00:02<00:08, 9.26it/s][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.4s finished
total_Changes in this round: 0.07282977245378465 average changes: 0.06600341413001043 0.0792040969560125
53%|██████████████████████████████████████████▉ | 53/100 [00:38<00:20, 2.26it/s]
I am peer 58 I started the attack, at round 5
79%|███████████████████████████████████████████████████████████████▉ | 79/100 [00:40<00:07, 2.92it/s]
I am peer 95 I started the attack, at round 5
100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:43<00:00, 2.30it/s]
Global model loss: 0.04791858909110321; global model accuracy: 0.9851178526878357 Round 6.
0%| | 0/100 [00:00<?, ?it/s]
I am peer 51 I started the attack, at round 6
49%|███████████████████████████████████████▋ | 49/100 [00:02<00:02, 18.14it/s]
I am peer 58 I started the attack, at round 6
74%|███████████████████████████████████████████████████████████▉ | 74/100 [00:05<00:01, 14.26it/s][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.4s finished
total_Changes in this round: 0.047182858753608475 average changes: 0.06223930305473005 0.07468716366567606
87%|██████████████████████████████████████████████████████████████████████▍ | 87/100 [00:21<00:05, 2.34it/s]
I am peer 95 I started the attack, at round 6
100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:24<00:00, 4.09it/s]
Global model loss: 0.12014329470220501; global model accuracy: 0.9583682417869568 Round 7.
0%| | 0/100 [00:00<?, ?it/s]
I am peer 95 I started the attack, at round 7
4%|███▎ | 4/100 [00:02<01:01, 1.56it/s]
I am peer 51 I started the attack, at round 7
7%|█████▋ | 7/100 [00:05<01:05, 1.43it/s][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.4s finished
total_Changes in this round: 0.04856604792580881 average changes: 0.059960427199909835 0.0719525126398918
55%|████████████████████████████████████████████▌ | 55/100 [00:44<00:33, 1.36it/s]
I am peer 58 I started the attack, at round 7
100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:47<00:00, 2.12it/s]
Global model loss: 0.06726779270004804; global model accuracy: 0.9827722907066345 Round 8.
56%|█████████████████████████████████████████████▎ | 56/100 [02:20<01:47, 2.43s/it][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.5s finished
total_Changes in this round: 0.085633505125058 average changes: 0.06362800976064527 0.07635361171277431
100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [04:24<00:00, 2.65s/it]
Global model loss: 6.405008795226287; global model accuracy: 0.5955941677093506 Round 9.
9%|███████▍ | 9/100 [00:22<03:47, 2.50s/it][Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1000 out of 1000 | elapsed: 1.4s finished
total_Changes in this round: 0.10100569015777777 average changes: 0.06830021981028685 0.08196026377234421
67%|██████████████████████████████████████████████████████▎ | 67/100 [08:34<01:26, 2.61s/it]
In [ ]:
# sort the feature according to the last epoch and print it with importances sort_index = np.argsort(FI_dic1[9]) for x in sort_index: print(names[x], ', ', FI_dic1[9][x])