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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 sectors with sensitive data.

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

  • Principles of data minimization and privacy.
  • Techniques for securing model updates (e.g., differential privacy).
  • Ensuring federated learning complies with data protection regulations.

Implementing Federated Learning

  • Setting up a federated learning environment.
  • Distributed model training using federated frameworks.
  • Considerations for performance and accuracy.

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 on 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

  • A foundational understanding of machine learning concepts.
  • Familiarity with the basics of data privacy and security.

Audience

  • Data scientists and AI researchers specializing in privacy-preserving machine learning.
  • Professionals in healthcare and finance who manage sensitive data.
  • IT and compliance managers interested in methods for secure AI collaboration.
 14 Hours

Number of participants


Price per participant

Open Training Courses require 5+ participants.

Upcoming Courses

Federated Learning for Secure AI Collaboration

2026-06-19 09:30
14 hours
Ankara
29025 TRY (Online)
29425 TRY (Classroom)

Federated Learning for Secure AI Collaboration

2026-07-03 09:30
14 hours
Antalya
29025 TRY (Online)
29425 TRY (Classroom)

Federated Learning for Secure AI Collaboration

2026-07-17 09:30
14 hours
Istanbul, Taksim
29025 TRY (Online)
29425 TRY (Classroom)

Federated Learning for Secure AI Collaboration

2026-07-31 09:30
14 hours
Ankara
29025 TRY (Online)
29425 TRY (Classroom)

Federated Learning for Secure AI Collaboration

2026-08-14 09:30
14 hours
Antalya
29025 TRY (Online)
29425 TRY (Classroom)

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