Feature Scaling of Training Set and Test Set
You do feature scaling for accelerating learning process. Features may have different scales. One maybe from 1 to 10 and one may be from -100 to 1000. Using normalization, you make the scale of them the same as each other, helps accelerate the learning process. You should find the mean and variance for each feature separately on your training data. then during training and testing each feature should be reduced by the corresponding mean and be divided by the corresponding standard deviation. So yes, for each feature during testing and training you have to provide same values for mean and std which are obtained using training data. I suggest you taking a look at here.