University · Artificial Intelligence · AI Ethics, Safety, and Alignment
Privacy-Preserving Machine Learning: Federated Learning and Differential Privacy
4 Abschnitte
Threat models for ML privacy (membership inference, model inversion, data reconstruction), federated learning architecture and aggregation, differential privacy formal definition and composition theorems, practical trade-offs between privacy and model utility, and applications in healthcare and mobile AI.
Inhaltsübersicht
- Privacy Threats in Machine Learning Systems
- Federated Learning: Architecture and Privacy Properties
- Differential Privacy: Formal Guarantees and Composition
- Applications, Ethical Implications, and Deployment Considerations
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