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 for Government
- Overview of Federated Learning
- Key concepts and benefits
- Federated Learning vs. traditional machine learning
Data Privacy and Security in AI for Government
- Understanding data privacy concerns in AI
- Regulatory frameworks and compliance (e.g., GDPR)
- Introduction to privacy-preserving techniques
Federated Learning Techniques for Government
- 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 for Government
- Federated Learning in healthcare
- Federated Learning in finance and banking
- Federated Learning in mobile and IoT devices
Advanced Topics in Federated Learning for Government
- Exploring Differential Privacy in Federated Learning
- Secure Aggregation and Encryption techniques
- Future directions and emerging trends
Case Studies and Practical Applications for Government
- 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 for Government
Requirements
- Understanding of machine learning fundamentals for government applications
- Basic knowledge of data privacy principles for government use
- Experience with Python programming for government projects
Audience
- Privacy engineers for government agencies
- AI ethics specialists for government initiatives
- Data privacy officers for government organizations
14 Hours
