F-divergence is a class of functions used to quantify the difference between two probability distributions, generalizing many well-known divergence measures like Kullback-Leibler divergence and Jensen-Shannon divergence. It is instrumental in various fields such as information theory, statistics, and machine learning, where understanding the discrepancy between distributions is crucial for tasks like hypothesis testing, model evaluation, and optimization.