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Course Outline
Introduction to Federated Learning
- Overview of Federated Learning
- Core concepts and key advantages
- Comparison between Federated Learning and traditional machine learning
Data Privacy and Security in AI
- Identifying data privacy concerns in AI systems
- Regulatory landscapes and compliance requirements (such as GDPR)
- Fundamentals of privacy-preserving techniques
Techniques in Federated Learning
- Building Federated Learning systems with Python and PyTorch
- Constructing privacy-preserving models via Federated Learning frameworks
- Addressing challenges in Federated Learning: communication, computation, and security
Real-World Applications of Federated Learning
- Use cases in the healthcare sector
- Applications in finance and banking
- Implementation on mobile and IoT devices
Advanced Concepts in Federated Learning
- Integrating Differential Privacy into Federated Learning
- Employing Secure Aggregation and Encryption methods
- Exploring future directions and emerging trends
Case Studies and Practical Exercises
- Case study: Deploying Federated Learning in a healthcare context
- Hands-on tasks using real-world datasets
- Practical project work and application scenarios
Conclusion and Future Recommendations
Requirements
- Foundational understanding of machine learning concepts
- Basic familiarity with data privacy standards
- Proficiency in Python programming
Target Audience
- Privacy engineers
- AI ethics specialists
- Data privacy officers
14 Hours
