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name: training description: "Routes TensorFlow Privacy users who want to train differentially private models with optimizers, Keras model wrappers, estimators, or logistic-regression helpers." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training

Use this sub-skill when the user wants to train a differentially private model or wrap an existing TensorFlow training loop with DP behavior.

Trigger phrases

• "use DP-SGD"
• "replace my optimizer with a DP optimizer"
• "train with TensorFlow Privacy"
• "wrap a Keras model for differential privacy"
• "DP estimator"
• "DP logistic regression"
• "vectorized optimizer"
• "sparse DP optimizer"

What this sub-skill covers

• DPKeras*Optimizer and generic DP optimizer wrappers
• DPModel / DPSequential / make_dp_model_class
• estimator-based classifiers
• logistic-regression helpers and noise-multiplier helpers
• the training-side guidance in the MNIST and text tutorials

What it does not cover

• privacy budget and noise calculation -> ../privacy-accounting/
• DPQuery internals -> ../queries/
• membership inference and secret-sharer analysis -> ../privacy-tests/
• fast clipping internals -> ../fast-clipping/

Read this before you act

• references/api-reference.md for verified constructors, function signatures, and return shapes.
• references/troubleshooting.md for loss-shape, microbatch, and optimizer-state failures.
• ../../references/install-and-scope.md for the minimum CPU runtime and the published package names.

Typical workflow

1. Pick the DP training surface that matches the user's code style:
   • Keras compile/fit -> DPKeras*Optimizer or DPModel
   • custom training loop -> DPKeras*Optimizer or a generic DP optimizer wrapper
   • estimator-style code -> DNNClassifier
   • logistic-regression experiment -> logistic_dpsgd or logistic_objective_perturbation
2. Confirm the loss is per-example when the DP optimizer needs it.
3. Choose the right num_microbatches and clipping norm.
4. Use the bundled tiny smoke script if you need a safe training sanity check.
5. If the model uses sparse or distributed paths, read the troubleshooting page before changing the training loop.

Cross-links

• ../fast-clipping/ owns the lower-level clipping internals and sparse-noise helpers used by DPModel.
• ../privacy-accounting/ owns the epsilon/noise calculation that usually follows training.

Bundled helper

Run scripts/tiny_dp_training_smoke.py when you want a small, deterministic training sanity check. It trains a tiny synthetic classifier with DPKerasSGDOptimizer and reports the final loss.