Knowledge Distillation is the mechanism by which the insights gained from a larger teacher model are transferred to a smaller student model.

On one hand, the local data of users may contain sensitive information, and privacy-preserving mechanisms are needed.

While agents' learning is data-driven, sampling from millions

The key quantity of interest here is the Riemannian metric, which characterizes the Riemannian geometry and defines straight lines and derivatives on the manifold.