We develop a general correspondence between a family of loss functions that act as surrogates to 0-1 loss, and the class of Ali-Silvey or $f$-divergence functionals. This correspondence provides the basis for choosing and evaluating various surrogate losses frequently used in statistical learning (e.g., hinge loss, exponential loss, logistic loss); conversely, it provides a decision-theoretic framework for the choice of divergences in signal processing and quantization theory. We exploit this correspondence to characterize the statistical behavior of a nonparametric decentralized hypothesis testing algorithms that operate by minimizing convex surrogate loss functions. In particular, we specify the family of loss functions that are equivalent to 0-1 loss in the sense of producing the same quantization rules and discriminant functions.