Federated Learning for Secure AI Collaboration

• 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

Federated Learning (FL) is an approach to training machine learning models across multiple decentralized devices or servers holding local data samples, without exchanging them. This distributed approach to training models while preserving data privacy is especially valuable in sectors with sensitive data, such as healthcare and finance. By enabling secure AI collaboration, federated learning facilitates robust model development while protecting individual privacy and meeting regulatory compliance standards.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI and data professionals who wish to understand and implement federated learning techniques for privacy-preserving machine learning and collaborative AI solutions across distributed data sources.

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

• Understand the core concepts and benefits of federated learning.
• Implement distributed training strategies for AI models.
• Apply federated learning techniques to secure data-sensitive collaborations.
• Explore case studies and practical examples of federated learning in healthcare and finance.

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

• 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