Federated Learning for Privacy-Preserving AI

• Understanding of machine learning fundamentals
• Basic knowledge of data privacy principles
• Experience with Python programming

Audience

• Privacy engineers
• AI ethics specialists
• Data privacy officers

Federated Learning enables the training of AI models across decentralized data sources without compromising privacy. This course explores how Federated Learning can be used to create privacy-preserving AI models, addressing the challenges and applications in this rapidly evolving field.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to understand and apply Federated Learning to ensure data privacy in AI development.

By the end of this training, participants will be able to:

• Understand the principles and benefits of Federated Learning.
• Implement privacy-preserving machine learning models using Federated Learning techniques.
• Address the challenges of data privacy in decentralized AI training.
• Apply Federated Learning in real-world scenarios across various industries.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Federated Learning

• Overview of Federated Learning
• 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
• 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
• Federated Learning in finance and banking
• Federated Learning in mobile and IoT devices

Advanced Topics in Federated Learning

• Exploring Differential Privacy in Federated Learning
• Secure Aggregation and Encryption techniques
• Future directions and emerging trends

Case Studies and Practical Applications

• 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