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# Licensed under the Apache License, Version 2.0 (the "License");
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# ==============================================================================
"""Federated Optimization (FedProx) [Li et al., 2018] strategy.
Paper: arxiv.org/abs/1812.06127
"""
from typing import Callable, Dict, List, Optional, Tuple
from flwr.common import FitIns, MetricsAggregationFn, NDArrays, Parameters, Scalar
from flwr.server.client_manager import ClientManager
from flwr.server.client_proxy import ClientProxy
from .fedavg import FedAvg
# pylint: disable=line-too-long
[docs]class FedProx(FedAvg):
r"""Federated Optimization strategy.
Implementation based on https://arxiv.org/abs/1812.06127
The strategy in itself will not be different than FedAvg, the client needs to
be adjusted.
A proximal term needs to be added to the loss function during the training:
.. math::
\\frac{\\mu}{2} || w - w^t ||^2
Where $w^t$ are the global parameters and $w$ are the local weights the function
will be optimized with.
In PyTorch, for example, the loss would go from:
.. code:: python
loss = criterion(net(inputs), labels)
To:
.. code:: python
for local_weights, global_weights in zip(net.parameters(), global_params):
proximal_term += (local_weights - global_weights).norm(2)
loss = criterion(net(inputs), labels) + (config["proximal_mu"] / 2) *
proximal_term
With `global_params` being a copy of the parameters before the training takes
place.
.. code:: python
global_params = copy.deepcopy(net).parameters()
Parameters
----------
fraction_fit : float, optional
Fraction of clients used during training. In case `min_fit_clients`
is larger than `fraction_fit * available_clients`, `min_fit_clients`
will still be sampled. Defaults to 1.0.
fraction_evaluate : float, optional
Fraction of clients used during validation. In case `min_evaluate_clients`
is larger than `fraction_evaluate * available_clients`,
`min_evaluate_clients` will still be sampled. Defaults to 1.0.
min_fit_clients : int, optional
Minimum number of clients used during training. Defaults to 2.
min_evaluate_clients : int, optional
Minimum number of clients used during validation. Defaults to 2.
min_available_clients : int, optional
Minimum number of total clients in the system. Defaults to 2.
evaluate_fn : Optional[Callable[[int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]]]
Optional function used for validation. Defaults to None.
on_fit_config_fn : Callable[[int], Dict[str, Scalar]], optional
Function used to configure training. Defaults to None.
on_evaluate_config_fn : Callable[[int], Dict[str, Scalar]], optional
Function used to configure validation. Defaults to None.
accept_failures : bool, optional
Whether or not accept rounds containing failures. Defaults to True.
initial_parameters : Parameters, optional
Initial global model parameters.
fit_metrics_aggregation_fn : Optional[MetricsAggregationFn]
Metrics aggregation function, optional.
evaluate_metrics_aggregation_fn : Optional[MetricsAggregationFn]
Metrics aggregation function, optional.
proximal_mu : float
The weight of the proximal term used in the optimization. 0.0 makes
this strategy equivalent to FedAvg, and the higher the coefficient, the more
regularization will be used (that is, the client parameters will need to be
closer to the server parameters during training).
"""
# pylint: disable=too-many-arguments,too-many-instance-attributes
def __init__(
self,
*,
fraction_fit: float = 1.0,
fraction_evaluate: float = 1.0,
min_fit_clients: int = 2,
min_evaluate_clients: int = 2,
min_available_clients: int = 2,
evaluate_fn: Optional[
Callable[
[int, NDArrays, Dict[str, Scalar]],
Optional[Tuple[float, Dict[str, Scalar]]],
]
] = None,
on_fit_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,
on_evaluate_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,
accept_failures: bool = True,
initial_parameters: Optional[Parameters] = None,
fit_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
proximal_mu: float,
) -> None:
super().__init__(
fraction_fit=fraction_fit,
fraction_evaluate=fraction_evaluate,
min_fit_clients=min_fit_clients,
min_evaluate_clients=min_evaluate_clients,
min_available_clients=min_available_clients,
evaluate_fn=evaluate_fn,
on_fit_config_fn=on_fit_config_fn,
on_evaluate_config_fn=on_evaluate_config_fn,
accept_failures=accept_failures,
initial_parameters=initial_parameters,
fit_metrics_aggregation_fn=fit_metrics_aggregation_fn,
evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation_fn,
)
self.proximal_mu = proximal_mu
def __repr__(self) -> str:
"""Compute a string representation of the strategy."""
rep = f"FedProx(accept_failures={self.accept_failures})"
return rep