I read that to compute the derivative of the error with respect to the input of a convolution layer is the same to make of a convolution between deltas of the next layer and the weight matrix rotated by $180°$, i.e. something like
$$\delta^l_{ij}=\delta^{l+1}_{ij} * rot180(W^{l+1})f'(x^l_{ij})$$
with $*$ convolution operator. This is valid with $stride=1$.
However, what happens when stride is greater than $1$? Is it still a convolution with a kernel rotation or I can't make this simplification?