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Course Outline
Introduction to Federated Learning
- Overview of the Federated Learning framework
- Core concepts and key benefits
- Comparing Federated Learning with traditional machine learning
Data Privacy and Security in AI
- Analyzing privacy concerns within AI systems
- Regulatory compliance and frameworks (e.g., GDPR)
- Introduction to techniques that preserve privacy
Federated Learning Techniques
- Implementing Federated Learning using Python and PyTorch
- Constructing privacy-preserving models with Federated Learning frameworks
- Overcoming challenges in communication, computation, and security
Real-World Applications of Federated Learning
- Applications in the healthcare sector
- Use cases in finance and banking
- Deployment on mobile and IoT devices
Advanced Topics in Federated Learning
- In-depth look at Differential Privacy in Federated Learning
- Techniques for Secure Aggregation and Encryption
- Future directions and emerging industry trends
Case Studies and Practical Applications
- Case study: Deploying Federated Learning in a healthcare environment
- Hands-on exercises using real-world datasets
- Practical implementation and project work
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning concepts
- Basic comprehension of data privacy standards
- Proficiency in Python programming
Target Audience
- Privacy engineers
- AI ethics specialists
- Data privacy officers
14 Hours
