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Course Outline
Introduction to Federated Learning
- Overview of Federated Learning
- Key concepts and benefits
- Federated Learning vs. traditional machine learning
Data Privacy and Security in AI
- Understanding data privacy concerns in AI
- Regulatory frameworks and compliance (e.g., GDPR)
- Introduction to privacy-preserving techniques
Federated Learning Techniques
- Implementing Federated Learning with Python and PyTorch
- Building privacy-preserving models using Federated Learning frameworks
- Challenges in Federated Learning: communication, computation, and security
Real-World Applications of Federated Learning
- Federated Learning in healthcare
- Federated Learning in finance and banking
- Federated Learning in mobile and IoT devices
Advanced Topics in Federated Learning
- Exploring Differential Privacy in Federated Learning
- Secure Aggregation and Encryption techniques
- Future directions and emerging trends
Case Studies and Practical Applications
- Case study: Implementing Federated Learning in a healthcare setting
- Hands-on exercises with real-world datasets
- Practical applications and project work
Summary and Next Steps
Requirements
- Understanding of machine learning fundamentals
- Basic knowledge of data privacy principles
- Experience with Python programming
Audience
- Privacy engineers
- AI ethics specialists
- Data privacy officers
14 Hours
