-
Notifications
You must be signed in to change notification settings - Fork 1
/
skd_threshold_25to50.py
181 lines (141 loc) · 8.35 KB
/
skd_threshold_25to50.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
import numpy as np
from tensorflow.keras.models import clone_model, load_model
from tensorflow.keras.callbacks import EarlyStopping
import tensorflow as tf
from data_utils import generate_alignment_data
from Neural_Networks import remove_last_layer
class FedMD():
def __init__(self, parties, public_dataset,
private_data, total_private_data,
private_test_data, N_alignment,
N_rounds,
N_logits_matching_round, logits_matching_batchsize,
N_private_training_round, private_training_batchsize):
self.N_parties = len(parties)
self.public_dataset = public_dataset
self.private_data = private_data
self.private_test_data = private_test_data
self.N_alignment = N_alignment
self.N_rounds = N_rounds
self.N_logits_matching_round = N_logits_matching_round
self.logits_matching_batchsize = logits_matching_batchsize
self.N_private_training_round = N_private_training_round
self.private_training_batchsize = private_training_batchsize
self.collaborative_parties = []
self.init_result = []
# train all models in their own private data
print("start model initialization: ")
for i in range(self.N_parties):
print("model ", i)
model_A_twin = None
model_A_twin = clone_model(parties[i]) # load private model
model_A_twin.set_weights(parties[i].get_weights())
model_A_twin.compile(optimizer=tf.keras.optimizers.Adam(lr = 1e-3),
loss = "sparse_categorical_crossentropy",
metrics = ["accuracy"])
print("start full stack training ... ")
# train private models with private data
model_A_twin.fit(private_data[i]["X"], private_data[i]["y"],
batch_size = 32, epochs = 25, shuffle=True, verbose = 0,
validation_data = [private_test_data["X"], private_test_data["y"]],
callbacks=[EarlyStopping(monitor='val_accuracy', min_delta=0.001, patience=10)]
)
print("full stack training done")
model_A = remove_last_layer(model_A_twin, loss="mean_absolute_error")
self.collaborative_parties.append({"model_logits": model_A,
"model_classifier": model_A_twin,
"model_weights": model_A_twin.get_weights()})
self.init_result.append({"val_acc": model_A_twin.history.history['val_accuracy'],
"train_acc": model_A_twin.history.history['accuracy'],
"val_loss": model_A_twin.history.history['val_loss'],
"train_loss": model_A_twin.history.history['loss'],
})
print()
del model_A, model_A_twin
#END FOR LOOP
# theoratical upper bound - acc if the models were trained with all private data
print("calculate the theoretical upper bounds for participants: ")
self.upper_bounds = []
self.pooled_train_result = []
for model in parties:
model_ub = clone_model(model)
model_ub.set_weights(model.get_weights())
model_ub.compile(optimizer=tf.keras.optimizers.Adam(lr = 1e-3),
loss = "sparse_categorical_crossentropy",
metrics = ["accuracy"])
model_ub.fit(total_private_data["X"], total_private_data["y"],
batch_size = 32, epochs = 50, shuffle=True, verbose = 0,
validation_data = [private_test_data["X"], private_test_data["y"]],
callbacks=[EarlyStopping(monitor="val_accuracy", min_delta=0.001, patience=10)])
self.upper_bounds.append(model_ub.history.history["val_accuracy"][-1])
self.pooled_train_result.append({"val_acc": model_ub.history.history["val_accuracy"],
"acc": model_ub.history.history["accuracy"]})
del model_ub
print("the upper bounds are:", self.upper_bounds)
def collaborative_training(self):
# start collaborating training
collaboration_performance = {i: [] for i in range(self.N_parties)}
r = 0
threshold_ratio = 0.25 # initially 25% models are selected
while True:
# At beginning of each round, generate new alignment dataset
# subset of MNIST train data of 5000 samples
alignment_data = generate_alignment_data(self.public_dataset["X"],
self.public_dataset["y"],
self.N_alignment)
print("round ", r)
print("update logits ... ")
# test performance
print("test performance ... ")
acc_and_indices = []
for index, d in enumerate(self.collaborative_parties):
y_pred = d["model_classifier"].predict(self.private_test_data["X"], verbose = 0).argmax(axis = 1)
acc = np.mean(self.private_test_data["y"] == y_pred)
collaboration_performance[index].append(acc)
acc_and_indices.append((acc, index))
print(collaboration_performance[index][-1])
del y_pred
# Sort the list of tuples (accuracy, index) based on accuracy
acc_and_indices.sort(key=lambda x: x[0], reverse=True)
# Get indices of top 'threshold_ratio' models
ind = [index for acc, index in acc_and_indices[:int(len(acc_and_indices)*threshold_ratio)]]
# update logits
logits = 0
count = 0
for index, d in enumerate(self.collaborative_parties):
if index in ind:
d["model_logits"].set_weights(d["model_weights"])
logits += d["model_logits"].predict(alignment_data["X"], verbose = 0)
count += 1
logits /= count
r+= 1
# Gradually increase the threshold up to 50%
threshold_ratio = min(0.50, threshold_ratio + 0.02)
if r > self.N_rounds:
break
print("updates models ...")
for index, d in enumerate(self.collaborative_parties):
print("model {0} starting alignment with public logits... ".format(index))
weights_to_use = None
weights_to_use = d["model_weights"]
d["model_logits"].set_weights(weights_to_use)
d["model_logits"].fit(alignment_data["X"], logits,
batch_size = self.logits_matching_batchsize,
epochs = self.N_logits_matching_round,
shuffle=True, verbose = 0)
d["model_weights"] = d["model_logits"].get_weights()
print("model {0} done alignment".format(index))
print("model {0} starting training with private data... ".format(index))
weights_to_use = None
weights_to_use = d["model_weights"]
d["model_classifier"].set_weights(weights_to_use)
d["model_classifier"].fit(self.private_data[index]["X"],
self.private_data[index]["y"],
batch_size = self.private_training_batchsize,
epochs = self.N_private_training_round,
shuffle=True, verbose = 0)
d["model_weights"] = d["model_classifier"].get_weights()
print("model {0} done private training. \n".format(index))
#END FOR LOOP
#END WHILE LOOP
return collaboration_performance