# Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from nvflare.apis.dxo import MetaKey, from_shareable
from nvflare.apis.filter import Filter
from nvflare.apis.fl_constant import ReturnCode
from nvflare.apis.fl_context import FLContext
from nvflare.apis.shareable import Shareable
[docs]class PercentilePrivacy(Filter):
def __init__(self, percentile=10, gamma=0.01):
"""Implementation of "largest percentile to share" privacy preserving policy.
Shokri and Shmatikov, Privacy-preserving deep learning, CCS '15
Args:
percentile (int, optional): Only abs diff greater than this percentile is updated.
Allowed range 0..100. Defaults to 10.
gamma (float, optional): The upper limit to truncate abs values of weight diff. Defaults to 0.01. Any weight diff with abs<gamma will become 0.
"""
super().__init__()
# must be in 0..100, only update abs diff greater than percentile
self.percentile = percentile
# must be positive
self.gamma = gamma # truncate absolute value of delta W
[docs] def process(self, shareable: Shareable, fl_ctx: FLContext) -> Shareable:
"""Compute the percentile on the abs delta_W.
Only share the params where absolute delta_W greater than
the percentile value
Args:
shareable: information from client
fl_ctx: context provided by workflow
Returns:
Shareable: a shareable containing the truncated weight diff
"""
self.log_debug(fl_ctx, "inside filter")
rc = shareable.get_return_code()
if rc != ReturnCode.OK:
# don't process if RC not OK
return shareable
try:
dxo = from_shareable(shareable)
except:
self.log_exception(fl_ctx, "shareable data is not a valid DXO")
return shareable
if dxo.data is None:
self.log_debug(fl_ctx, "no data to filter")
return shareable
self.logger.debug("check gamma")
if self.gamma <= 0:
self.log_debug(fl_ctx, "no partial model: gamma: {}".format(self.gamma))
return shareable # do nothing
if self.percentile < 0 or self.percentile > 100:
self.log_debug(fl_ctx, "no partial model: percentile: {}".format(self.percentile))
return shareable # do nothing
# invariant to local steps
model_diff = dxo.data
total_steps = dxo.get_meta_prop(MetaKey.NUM_STEPS_CURRENT_ROUND, 1)
delta_w = {name: model_diff[name] / total_steps for name in model_diff}
# abs delta
all_abs_values = np.concatenate([np.abs(delta_w[name].ravel()) for name in delta_w])
cutoff = np.percentile(a=all_abs_values, q=self.percentile, overwrite_input=False)
self.log_info(
fl_ctx,
f"Max abs delta_w: {np.max(all_abs_values)}, Min abs delta_w: {np.min(all_abs_values)},"
f"cutoff: {cutoff}, scale: {total_steps}.",
)
for name in delta_w:
diff_w = delta_w[name]
if np.ndim(diff_w) == 0: # single scalar, no clipping
delta_w[name] = diff_w * total_steps
continue
selector = (diff_w > -cutoff) & (diff_w < cutoff)
diff_w[selector] = 0.0
diff_w = np.clip(diff_w, a_min=-self.gamma, a_max=self.gamma)
delta_w[name] = diff_w * total_steps
dxo.data = delta_w
return dxo.update_shareable(shareable)