non_delayed_index = index_collection[:usual_node_NUMBER]
print('non_delayed_index')
print(len(non_delayed_index))
delayed_index=index_collection[usual_node_NUMBER:usual_node_NUMBER+delayed_node_NUMBER]
delayed_speed=delayed_SPEED
print('delayed_index')
print(len(delayed_index))
shared_index=index_collection[usual_node_NUMBER+delayed_node_NUMBER:usual_node_NUMBER+delayed_node_NUMBER+shared_node_NUMBER]
print('shared_index')
print(len(shared_index))
# initialize
train = Fed_Training(model,non_delayed_index,central_weight_path,x_test, y_test,batch_size=50,augument=my_AUGUMENT,local_iid=local_IID,node_evl=node_EVL)
train.load_test_set()
# set bad nodes
if bad_NODE:
if bad_node_nb!=0:
if bad_node_size!=0:
bad_success = train.set_bad_node(bad_node_nb,bad_node_size)
print('if bad_node assigned?')
print(str(bad_success))
# set delayed index
if delayed_index!=[]:
train.set_delayed_update(delayed_index,delayed_speed)
# set shared index
delayed_node_NUMBER]
delayed_speed = delayed_SPEED
print('delayed_index')
print(len(delayed_index))
shared_index = index_collection[usual_node_NUMBER +
delayed_node_NUMBER:usual_node_NUMBER +
delayed_node_NUMBER + shared_node_NUMBER]
print('shared_index')
print(len(shared_index))
# initialize
train = Fed_Training(model,
non_delayed_index,
central_weight_path,
x_test,
y_test,
batch_size=50,
augument=False)
train.load_test_set()
# set delayed index
if delayed_index != []:
train.set_delayed_update(delayed_index, delayed_speed)
# set shared index
if shared_index != []:
train.set_shared_index(shared_index)
# do training
epo_h, loss_h, acc_h = train.fit(epo, patience, local_epoch=loc_epoch)