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#
# 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
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# See the License for the specific language governing permissions and
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import torch
from torch.nn.modules.loss import _Loss
[docs]
class PTFedProxLoss(_Loss):
def __init__(self, mu: float = 0.01) -> None:
"""Compute FedProx loss: a loss penalizing the deviation from global model.
Args:
mu: weighting parameter
"""
super().__init__()
if mu < 0.0:
raise ValueError("mu should be no less than 0.0")
self.mu = mu
[docs]
def forward(self, input, target) -> torch.Tensor:
"""Forward pass in training.
Args:
input (nn.Module): the local pytorch model
target (nn.Module): the copy of global pytorch model when local clients received it
at the beginning of each local round
Returns:
FedProx loss term
"""
prox_loss: torch.Tensor = 0.0
for param, ref in zip(input.named_parameters(), target.named_parameters()):
prox_loss += (self.mu / 2) * torch.sum((param[1] - ref[1]) ** 2)
return prox_loss