Advanced
Differential Privacy in LLMs
DP adds calibrated noise to training, preventing memorization of individual data points.
Core Skills
Fundamental abilities you'll develop
- Implement DP-SGD for LLM training.
Learning Goals
What you'll understand and learn
- Analyze trade-offs in utility vs. privacy budget (epsilon).
- Evaluate memorization risks in non-DP vs. DP models.
Practical Skills
Hands-on techniques and methods
- Define differential privacy (DP) and its privacy guarantees.
- Train a DP LLM from scratch using libraries.
Advanced Level
Multi-layered Concepts
🚀 Enterprise Ready
Advanced Content Notice
This lesson covers advanced AI concepts and techniques. Strong foundational knowledge of AI fundamentals and intermediate concepts is recommended.
Differential Privacy in LLMs
Introduction
DP adds calibrated noise to training, preventing memorization of individual data points.
Key Concepts
- DP Definition: Output distributions similar whether any single record is included/excluded.
- DP-SGD: Clip gradients, add Gaussian noise.
- Privacy Budget: Epsilon measures leakage (lower = stronger privacy).
Implementation Steps
- Setup Opacus (PyTorch DP lib):
from opacus import PrivacyEngine privacy_engine = PrivacyEngine() model, optimizer, dataloader = privacy_engine.make_private( module=model, optimizer=optimizer, data_loader=dataloader, noise_multiplier=1.1, max_grad_norm=1.0 ) - Train with DP:
- Standard loop with privacy engine.
- Tune Hyperparams:
- Balance epsilon (e.g., 1-10) with accuracy.
- Inference: No changes; privacy in training.
Example
Train GPT-2 variant on sensitive text: DP reduces exact memorization by 90%.
Evaluation
- Metrics: Epsilon via accountants; utility via downstream tasks.
- Trade-offs: Noise degrades performance; scale data to mitigate.
Conclusion
DP-LLMs enable privacy-preserving AI; combine with federated learning for stronger guarantees.
Master Advanced AI Concepts
You're working with cutting-edge AI techniques. Continue your advanced training to stay at the forefront of AI technology.