# Copyright 2024 Flower Labs GmbH. 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.
# ==============================================================================
"""Central differential privacy with fixed clipping.
Papers: https://arxiv.org/abs/1712.07557, https://arxiv.org/abs/1710.06963
"""
from logging import INFO, WARNING
from typing import Dict, List, Optional, Tuple, Union
from flwr.common import (
EvaluateIns,
EvaluateRes,
FitIns,
FitRes,
NDArrays,
Parameters,
Scalar,
ndarrays_to_parameters,
parameters_to_ndarrays,
)
from flwr.common.differential_privacy import (
add_gaussian_noise_to_params,
compute_clip_model_update,
compute_stdv,
)
from flwr.common.differential_privacy_constants import (
CLIENTS_DISCREPANCY_WARNING,
KEY_CLIPPING_NORM,
)
from flwr.common.logger import log
from flwr.server.client_manager import ClientManager
from flwr.server.client_proxy import ClientProxy
from flwr.server.strategy.strategy import Strategy
[docs]class DifferentialPrivacyServerSideFixedClipping(Strategy):
"""Strategy wrapper for central DP with server-side fixed clipping.
Parameters
----------
strategy : Strategy
The strategy to which DP functionalities will be added by this wrapper.
noise_multiplier : float
The noise multiplier for the Gaussian mechanism for model updates.
A value of 1.0 or higher is recommended for strong privacy.
clipping_norm : float
The value of the clipping norm.
num_sampled_clients : int
The number of clients that are sampled on each round.
Examples
--------
Create a strategy:
>>> strategy = fl.server.strategy.FedAvg( ... )
Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping wrapper
>>> dp_strategy = DifferentialPrivacyServerSideFixedClipping(
>>> strategy, cfg.noise_multiplier, cfg.clipping_norm, cfg.num_sampled_clients
>>> )
"""
# pylint: disable=too-many-arguments,too-many-instance-attributes
def __init__(
self,
strategy: Strategy,
noise_multiplier: float,
clipping_norm: float,
num_sampled_clients: int,
) -> None:
super().__init__()
self.strategy = strategy
if noise_multiplier < 0:
raise ValueError("The noise multiplier should be a non-negative value.")
if clipping_norm <= 0:
raise ValueError("The clipping norm should be a positive value.")
if num_sampled_clients <= 0:
raise ValueError(
"The number of sampled clients should be a positive value."
)
self.noise_multiplier = noise_multiplier
self.clipping_norm = clipping_norm
self.num_sampled_clients = num_sampled_clients
self.current_round_params: NDArrays = []
def __repr__(self) -> str:
"""Compute a string representation of the strategy."""
rep = "Differential Privacy Strategy Wrapper (Server-Side Fixed Clipping)"
return rep
[docs] def initialize_parameters(
self, client_manager: ClientManager
) -> Optional[Parameters]:
"""Initialize global model parameters using given strategy."""
return self.strategy.initialize_parameters(client_manager)
[docs] def aggregate_fit(
self,
server_round: int,
results: List[Tuple[ClientProxy, FitRes]],
failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
"""Compute the updates, clip, and pass them for aggregation.
Afterward, add noise to the aggregated parameters.
"""
if failures:
return None, {}
if len(results) != self.num_sampled_clients:
log(
WARNING,
CLIENTS_DISCREPANCY_WARNING,
len(results),
self.num_sampled_clients,
)
for _, res in results:
param = parameters_to_ndarrays(res.parameters)
# Compute and clip update
compute_clip_model_update(
param, self.current_round_params, self.clipping_norm
)
log(
INFO,
"aggregate_fit: parameters are clipped by value: %.4f.",
self.clipping_norm,
)
# Convert back to parameters
res.parameters = ndarrays_to_parameters(param)
# Pass the new parameters for aggregation
aggregated_params, metrics = self.strategy.aggregate_fit(
server_round, results, failures
)
# Add Gaussian noise to the aggregated parameters
if aggregated_params:
aggregated_params = add_gaussian_noise_to_params(
aggregated_params,
self.noise_multiplier,
self.clipping_norm,
self.num_sampled_clients,
)
log(
INFO,
"aggregate_fit: central DP noise with "
"standard deviation: %.4f added to parameters.",
compute_stdv(
self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
),
)
return aggregated_params, metrics
[docs] def aggregate_evaluate(
self,
server_round: int,
results: List[Tuple[ClientProxy, EvaluateRes]],
failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
) -> Tuple[Optional[float], Dict[str, Scalar]]:
"""Aggregate evaluation losses using the given strategy."""
return self.strategy.aggregate_evaluate(server_round, results, failures)
[docs] def evaluate(
self, server_round: int, parameters: Parameters
) -> Optional[Tuple[float, Dict[str, Scalar]]]:
"""Evaluate model parameters using an evaluation function from the strategy."""
return self.strategy.evaluate(server_round, parameters)
[docs]class DifferentialPrivacyClientSideFixedClipping(Strategy):
"""Strategy wrapper for central DP with client-side fixed clipping.
Use `fixedclipping_mod` modifier at the client side.
In comparison to `DifferentialPrivacyServerSideFixedClipping`,
which performs clipping on the server-side, `DifferentialPrivacyClientSideFixedClipping`
expects clipping to happen on the client-side, usually by using the built-in
`fixedclipping_mod`.
Parameters
----------
strategy : Strategy
The strategy to which DP functionalities will be added by this wrapper.
noise_multiplier : float
The noise multiplier for the Gaussian mechanism for model updates.
A value of 1.0 or higher is recommended for strong privacy.
clipping_norm : float
The value of the clipping norm.
num_sampled_clients : int
The number of clients that are sampled on each round.
Examples
--------
Create a strategy:
>>> strategy = fl.server.strategy.FedAvg(...)
Wrap the strategy with the `DifferentialPrivacyClientSideFixedClipping` wrapper:
>>> dp_strategy = DifferentialPrivacyClientSideFixedClipping(
>>> strategy, cfg.noise_multiplier, cfg.clipping_norm, cfg.num_sampled_clients
>>> )
On the client, add the `fixedclipping_mod` to the client-side mods:
>>> app = fl.client.ClientApp(
>>> client_fn=client_fn, mods=[fixedclipping_mod]
>>> )
"""
# pylint: disable=too-many-arguments,too-many-instance-attributes
def __init__(
self,
strategy: Strategy,
noise_multiplier: float,
clipping_norm: float,
num_sampled_clients: int,
) -> None:
super().__init__()
self.strategy = strategy
if noise_multiplier < 0:
raise ValueError("The noise multiplier should be a non-negative value.")
if clipping_norm <= 0:
raise ValueError("The clipping threshold should be a positive value.")
if num_sampled_clients <= 0:
raise ValueError(
"The number of sampled clients should be a positive value."
)
self.noise_multiplier = noise_multiplier
self.clipping_norm = clipping_norm
self.num_sampled_clients = num_sampled_clients
def __repr__(self) -> str:
"""Compute a string representation of the strategy."""
rep = "Differential Privacy Strategy Wrapper (Client-Side Fixed Clipping)"
return rep
[docs] def initialize_parameters(
self, client_manager: ClientManager
) -> Optional[Parameters]:
"""Initialize global model parameters using given strategy."""
return self.strategy.initialize_parameters(client_manager)
[docs] def aggregate_fit(
self,
server_round: int,
results: List[Tuple[ClientProxy, FitRes]],
failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
"""Add noise to the aggregated parameters."""
if failures:
return None, {}
if len(results) != self.num_sampled_clients:
log(
WARNING,
CLIENTS_DISCREPANCY_WARNING,
len(results),
self.num_sampled_clients,
)
# Pass the new parameters for aggregation
aggregated_params, metrics = self.strategy.aggregate_fit(
server_round, results, failures
)
# Add Gaussian noise to the aggregated parameters
if aggregated_params:
aggregated_params = add_gaussian_noise_to_params(
aggregated_params,
self.noise_multiplier,
self.clipping_norm,
self.num_sampled_clients,
)
log(
INFO,
"aggregate_fit: central DP noise with "
"standard deviation: %.4f added to parameters.",
compute_stdv(
self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
),
)
return aggregated_params, metrics
[docs] def aggregate_evaluate(
self,
server_round: int,
results: List[Tuple[ClientProxy, EvaluateRes]],
failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
) -> Tuple[Optional[float], Dict[str, Scalar]]:
"""Aggregate evaluation losses using the given strategy."""
return self.strategy.aggregate_evaluate(server_round, results, failures)
[docs] def evaluate(
self, server_round: int, parameters: Parameters
) -> Optional[Tuple[float, Dict[str, Scalar]]]:
"""Evaluate model parameters using an evaluation function from the strategy."""
return self.strategy.evaluate(server_round, parameters)