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.
Duration 14 hours
Course Outline
Introduction to Federated Learning
- Overview of the Federated Learning paradigm
- Core concepts and strategic benefits
- Comparison between Federated Learning and conventional machine learning
Data Privacy and Security in AI
- Identifying data privacy concerns in AI systems
- Navigating regulatory frameworks and compliance standards (e.g., GDPR)
- Introduction to privacy-enhancing technologies
Federated Learning Techniques
- Implementing Federated Learning using Python and PyTorch
- Developing privacy-preserving models with Federated Learning frameworks
- Managing Federated Learning challenges: communication, computation, and security
Real-World Applications of Federated Learning
- Applying Federated Learning in the healthcare sector
- Use cases in finance and banking
- Implementation on mobile and IoT devices
Advanced Topics in Federated Learning
- Integration of Differential Privacy into Federated Learning
- Techniques for Secure Aggregation and Encryption
- Future trends and emerging directions
Case Studies and Practical Applications
- Case study: Deploying Federated Learning in a healthcare environment
- Practical exercises utilizing real-world datasets
- Application of learned concepts and project work
Summary and Next Steps
Requirements
- Foundational understanding of machine learning
- Basic grasp of data privacy principles
- Proficiency in Python programming
Target Audience
- Privacy engineers
- AI ethics specialists
- Data privacy officers
