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label-based Tree model for vfl #528

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merged 6 commits into from
Feb 22, 2023
Merged

label-based Tree model for vfl #528

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c36f220
dev label model xgb
xieyxclack Feb 20, 2023
eb59b57
add label protection method he
xieyxclack Feb 21, 2023
1cc8002
add unittest and baseline yaml
xieyxclack Feb 21, 2023
4ca2aac
bugfix: do not add self-loop in comm_manager.neighbors
xieyxclack Feb 21, 2023
39f588e
minor fix
xieyxclack Feb 21, 2023
5e42cf7
distinguish msg_buffer for train and eval
xieyxclack Feb 21, 2023
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bugfix: do not add self-loop in comm_manager.neighbors
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@xieyxclack
xieyxclack committed Feb 21, 2023
commit 4ca2aac25e04c065271b821076219c2786e8eebb
11 changes: 1 addition & 10 deletions federatedscope/vertical_fl/xgb_base/worker/XGBClient.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,15 +38,6 @@ def __init__(self,
self.feature_importance = [0] * self.num_of_feature

self._init_data_related_var()
# Add self-loop
if self._cfg.federate.mode == 'distributed':
self.comm_manager.add_neighbors(neighbor_id=self.ID,
address={
'host': self.comm_manager.host,
'port': self.comm_manager.port
})
else:
self.comm_manager.add_neighbors(neighbor_id=self.ID, address=None)

self.register_handlers('model_para', self.callback_func_for_model_para)
self.register_handlers('data_sample',
Expand Down Expand Up @@ -126,7 +117,7 @@ def start_a_new_training_round(self,
self.state = tree_num
receiver = [
each for each in list(self.comm_manager.neighbors.keys())
if each not in [self.ID, self.server_id]
if each != self.server_id
]
self.comm_manager.send(
Message(msg_type='data_sample',
Expand Down
35 changes: 22 additions & 13 deletions federatedscope/vertical_fl/xgb_base/worker/evaluation_wrapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,7 +60,7 @@ def _feedback_eval_metrics(self):
receiver=[
each
for each in list(self.comm_manager.neighbors.keys())
if each not in [self.server_id, self.ID]
if each != self.server_id
],
content='None'))

Expand All @@ -87,12 +87,17 @@ def _test_for_node(self, tree_num, node_num):
self._test_for_node(tree_num, node_num + 1)
# Other client owns the weight, need to communicate
elif self.model[tree_num][node_num].member:
self.comm_manager.send(
Message(msg_type='split_request',
sender=self.ID,
state=self.state,
receiver=[self.model[tree_num][node_num].member],
content=(tree_num, node_num)))
send_message = Message(
msg_type='split_request',
sender=self.ID,
state=self.state,
receiver=[self.model[tree_num][node_num].member],
content=(tree_num, node_num))
if self.model[tree_num][node_num].member == self.ID:
self.callback_func_for_split_request(send_message)
else:
self.comm_manager.send(send_message)

else:
self._test_for_node(tree_num, node_num + 1)

Expand All @@ -105,12 +110,16 @@ def callback_func_for_split_request(self, message: Message):
feature_value = self.model[tree_num][node_num].feature_value
left_child, right_child = self.model[tree_num].split_childern(
self.test_x[:, feature_idx], feature_value)
self.comm_manager.send(
Message(msg_type='split_result',
sender=self.ID,
state=self.state,
receiver=[sender],
content=(tree_num, node_num, left_child, right_child)))
send_message = Message(msg_type='split_result',
sender=self.ID,
state=self.state,
receiver=[sender],
content=(tree_num, node_num, left_child,
right_child))
if sender == self.ID:
self.callback_func_for_split_result(send_message)
else:
self.comm_manager.send(send_message)

def callback_func_for_split_result(self, message: Message):
tree_num, node_num, left_child, right_child = message.content
Expand Down
91 changes: 53 additions & 38 deletions federatedscope/vertical_fl/xgb_base/worker/train_wrapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,17 +28,18 @@ def train(self, tree_num, node_num=None, training_info=None):
self._find_and_send_split(split_ref, tree_num, node_num)
elif train_flag == 'call_for_local_gain':
g, h, tree_num, node_num = results
self.comm_manager.send(
Message(
msg_type='grad_and_hess',
sender=self.ID,
state=self.state,
receiver=[
each
for each in list(self.comm_manager.neighbors.keys())
if each != self.server_id
],
content=(tree_num, node_num, g, h)))
send_message = Message(
msg_type='grad_and_hess',
sender=self.ID,
state=self.state,
receiver=[
each for each in list(self.comm_manager.neighbors.keys())
if each != self.server_id
],
content=(tree_num, node_num, g, h))
self.comm_manager.send(send_message)
# imitate send this message to itself
self.callback_funcs_for_grad_and_hess(send_message)
else:
raise ValueError(f'The handler of {train_flag} is not defined.')

Expand All @@ -60,13 +61,17 @@ def _find_and_send_split(self, split_ref, tree_num, node_num):

split_ref['feature_idx'] -= accum_dim
split_child = False
self.comm_manager.send(
Message(msg_type='split',
sender=self.ID,
state=self.state,
receiver=[client_id],
content=(tree_num, node_num, split_ref,
split_child)))
send_message = Message(msg_type='split',
sender=self.ID,
state=self.state,
receiver=[client_id],
content=(tree_num, node_num, split_ref,
split_child))
if client_id == self.ID:
self.callback_func_for_split(send_message)
else:
self.comm_manager.send(send_message)

break
else:
accum_dim += dim
Expand Down Expand Up @@ -105,12 +110,15 @@ def callback_func_for_split(self, message: Message):
else:
content = (tree_num, node_num)

self.comm_manager.send(
Message(msg_type='continue_training',
sender=self.ID,
state=self.state,
receiver=[sender],
content=content))
send_message = Message(msg_type='continue_training',
sender=self.ID,
state=self.state,
receiver=[sender],
content=content)
if sender == self.ID:
self.callback_funcs_for_continue_training(send_message)
else:
self.comm_manager.send(send_message)

def callback_funcs_for_continue_training(self, message: Message):
if len(message.content) == 4:
Expand All @@ -128,13 +136,17 @@ def callback_funcs_for_grad_and_hess(self, message: Message):
tree_num, node_num, g, h)
if 'feature_idx' in split_info and 'value_idx' in split_info:
self.trainer.split_ref = split_info
self.comm_manager.send(
Message(msg_type='local_best_gain',
sender=self.ID,
state=self.state,
receiver=[message.sender],
content=(tree_num, node_num, best_gain, split_info,
improved_flag)))

send_message = Message(msg_type='local_best_gain',
sender=self.ID,
state=self.state,
receiver=[message.sender],
content=(tree_num, node_num, best_gain,
split_info, improved_flag))
if message.sender == self.ID:
self.callback_funcs_for_local_best_gain(send_message)
else:
self.comm_manager.send(send_message)

def callback_funcs_for_local_best_gain(self, message: Message):
tree_num, node_num, local_best_gain, split_info, improved_flag = \
Expand All @@ -150,13 +162,16 @@ def callback_funcs_for_local_best_gain(self, message: Message):
if max_gain is not None:
self.model[tree_num][node_num].member = split_client_id
split_child = True
self.comm_manager.send(
Message(msg_type='split',
sender=self.ID,
state=self.state,
receiver=[split_client_id],
content=(tree_num, node_num, split_ref,
split_child)))
send_message = Message(msg_type='split',
sender=self.ID,
state=self.state,
receiver=[split_client_id],
content=(tree_num, node_num, split_ref,
split_child))
if split_client_id == self.ID:
self.callback_func_for_split(send_message)
else:
self.comm_manager.send(send_message)
else:
self.trainer._set_weight_and_status(tree_num, node_num)

Expand Down