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
- What is federated learning, and how does it differ from centralized learning?
- Advantages of federated learning for secure AI collaboration
- Use cases and applications in sensitive data sectors
Core Components of Federated Learning
- Federated data, clients, and model aggregation
- Communication protocols and updates
- Handling heterogeneity in federated environments
Data Privacy and Security in Federated Learning
- Data minimization and privacy principles
- Techniques for securing model updates (e.g., differential privacy)
- Federated learning in compliance with data protection regulations
Implementing Federated Learning
- Setting up a federated learning environment
- Distributed model training with federated frameworks
- Performance and accuracy considerations
Federated Learning in Healthcare
- Secure data sharing and privacy concerns in healthcare
- Collaborative AI for medical research and diagnosis
- Case studies: federated learning in medical imaging and diagnosis
Federated Learning in Finance
- Using federated learning for secure financial modeling
- Fraud detection and risk analysis with federated approaches
- Case studies in secure data collaboration within financial institutions
Challenges and Future of Federated Learning
- Technical and operational challenges in federated learning
- Future trends and advancements in federated AI
- Exploring opportunities for federated learning across industries
Summary and Next Steps
Requirements
- Basic understanding of machine learning concepts
- Familiarity with data privacy and security fundamentals
Audience
- Data scientists and AI researchers focused on privacy-preserving machine learning
- Healthcare and finance professionals handling sensitive data
- IT and compliance managers interested in secure AI collaboration methods
14 Hours
