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 Duration 14 hours

Course Outline

Introduction to Federated Learning

  • Defining federated learning and its distinction from centralized learning
  • Benefits of federated learning for secure AI collaboration
  • Applications and use cases in data-sensitive sectors

Key Components of Federated Learning

  • Federated data, client structures, and model aggregation
  • Communication protocols and update mechanisms
  • Managing heterogeneity within federated systems

Data Privacy and Security in Federated Learning

  • Principles of data minimization and privacy preservation
  • Strategies for securing model updates (e.g., differential privacy)
  • Aligning federated learning with data protection regulations

Implementation of Federated Learning

  • Configuring a federated learning environment
  • Training distributed models using federated frameworks
  • Balancing performance and accuracy considerations

Federated Learning in Healthcare

  • Navigating secure data sharing and privacy issues in healthcare
  • Leveraging collaborative AI for medical research and diagnosis
  • Examining case studies: federated learning in medical imaging and diagnostics

Federated Learning in Finance

  • Applying federated learning to secure financial modeling
  • Conducting fraud detection and risk analysis via federated methods
  • Analyzing case studies of secure data collaboration in financial institutions

Challenges and the Future of Federated Learning

  • Addressing technical and operational hurdles in federated learning
  • Identifying future trends and advancements in federated AI
  • Discovering cross-industry opportunities for federated learning

Conclusion and Next Steps

Requirements

  • Foundational knowledge of machine learning concepts
  • Understanding of data privacy and security basics

Target Audience

  • Data scientists and AI researchers specializing in privacy-preserving machine learning
  • Professionals in healthcare and finance managing sensitive data
  • IT and compliance managers seeking secure AI collaboration strategies

Number of participants


Price per participant (excl. VAT)

Open Training Courses require 5+ participants.

Upcoming Courses

Federated Learning for Secure AI Collaboration

2026-12-28 09:30
14 hours
Munich
3472 EUR (Online)
3872 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2027-01-11 09:30
14 hours
Köln
3472 EUR (Online)
3872 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2027-01-25 09:30
14 hours
Frankfurt am Main
3472 EUR (Online)
3872 EUR (Classroom)

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