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FedMD.py
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FedMD.py
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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 = []
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])
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 ... ")
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
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
while True:
# At beginning of each round, generate new alignment dataset
alignment_data = generate_alignment_data(self.public_dataset["X"],
self.public_dataset["y"],
self.N_alignment)
print("round ", r)
print("update logits ... ")
# update logits
logits = 0
for d in self.collaborative_parties:
d["model_logits"].set_weights(d["model_weights"])
logits += d["model_logits"].predict(alignment_data["X"], verbose = 0)
logits /= self.N_parties
# test performance
print("test performance ... ")
for index, d in enumerate(self.collaborative_parties):
y_pred = d["model_classifier"].predict(self.private_test_data["X"], verbose = 0).argmax(axis = 1)
collaboration_performance[index].append(np.mean(self.private_test_data["y"] == y_pred))
print(collaboration_performance[index][-1])
del y_pred
r+= 1
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