dp sgd

Privacy starts as a governance requirement and turns into a training-time technique. What lands is knowing what differential privacy actually promises (and what it costs), plus how federated learning and DP fit together. Here is the rigorous, honest answer.

Epsilon is the privacy knob, and shipping DP means picking a number, justifying it, and tracking what each query spends. The signal is treating epsilon as a finite budget that composes, not a magic constant. Here is the answer.

Picking epsilon is the easy half. The hard half is the accuracy you just gave up, and the levers that get it back are not the ones most candidates reach for.