Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Federated Learning
- Overview of Federated Learning for government
- 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 for government
- 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 for government
- Federated Learning in finance and banking for government
- Federated Learning in mobile and IoT devices for government
Advanced Topics in Federated Learning
- Exploring Differential Privacy in Federated Learning for government
- Secure Aggregation and Encryption techniques for government
- Future directions and emerging trends for government
Case Studies and Practical Applications
- Case study: Implementing Federated Learning in a healthcare setting for government
- Hands-on exercises with real-world datasets for government
- Practical applications and project work for government
Summary and Next Steps
Requirements
- Proficiency in core machine learning concepts
- Familiarity with foundational data privacy frameworks
- Practical experience utilizing Python for development tasks
Audience
- Privacy engineers
- AI ethics specialists
- Data privacy officers
14 Hours
