loss.py

# *************************************************************************
# Copyright 2023 ByteDance and/or its affiliates
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# Copyright 2023 FedDecorr Authors
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import torch
import torch.nn as nn


class FedDecorrLoss(nn.Module):

    def __init__(self):
        super(FedDecorrLoss, self).__init__()
        self.eps = 1e-8

    def _off_diagonal(self, mat):
        # return a flattened view of the off-diagonal elements of a square matrix
        n, m = mat.shape
        assert n == m
        return mat.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()

    def forward(self, x):
        N, C = x.shape
        if N == 1:
            return 0.0

        x = x - x.mean(dim=0, keepdim=True)
        x = x / torch.sqrt(self.eps + x.var(dim=0, keepdim=True))

        corr_mat = torch.matmul(x.t(), x)

        loss = (self._off_diagonal(corr_mat).pow(2)).mean()
        loss = loss / N

        return loss