in binary classification where class labels are {-1, 1} is preprocessing needed?
You need {0,1} or {-1,1} labels depending on the output of your model. If you have a Sigmoid output use {0,1}, while TanH outputs work with {-1,1}.
No label choice is inherently right or wrong, as long as it's compatible with your model architecture and gives you good results.

EDIT:
In case of logistic regression you must use {0,1}, that is because this class of models has a Sigmoid output. Sigmoid function is always bounded in [0,1] and it can't take values outside of that range. It could never reach -1 and training won't work.