Standardize one predictor variable or all predictor variables to solve multi-collinearity

I was using a fixed-effects panel model with interaction effects when I realized that the VIF values are too high for some variables. I was advised to standardize the predictor variables to mitigate multi-collinearity. My question is that can I standardize just 1 predictor variable or must I standardize all predictor variables?
If so, are there any academic sources/literature that I could refer to for this matter?
Thanks so much in advance.


Less is more. Stay pure. Stay poor.
Two comments, I think of standardizing as a hard core version of centering and centering is usually recommended for interactions as well. You can do whatever you want, just be able to concisely describe your results to others. Meaning some times it is easy to apply something across the board.

My second comment is usually, why are variables collinear, what is the causal or data generating function between them? Lots of literature out there on including instruments, mediators, etc. in the model and inflation of variability. Would a partial Markovian independence presumption allow you to drop a variable. This may be influenced by the purpose of the analyses as well - inference vs. prediction.