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Course Outline

Introduction to Federated Learning

  • Defining federated learning and distinguishing it from centralized learning approaches.
  • The benefits of federated learning for secure AI collaboration.
  • Real-world use cases and applications in sectors with sensitive data.

Core Components of Federated Learning

  • Understanding federated data, clients, and the model aggregation process.
  • Communication protocols and the mechanism of updates.
  • Managing heterogeneity within federated environments.

Data Privacy and Security in Federated Learning

  • Principles of data minimization and privacy preservation.
  • Methods for securing model updates, such as differential privacy.
  • Aligning federated learning with data protection regulations.

Implementing Federated Learning

  • Establishing a functional federated learning environment.
  • Conducting distributed model training using federated frameworks.
  • Evaluating performance and accuracy factors.

Federated Learning in Healthcare

  • Navigating secure data sharing and privacy issues in healthcare.
  • Leveraging collaborative AI for medical research and diagnosis.
  • Case studies: Applications in medical imaging and diagnostic processes.

Federated Learning in Finance

  • Employing federated learning for secure financial modeling.
  • Enhancing fraud detection and risk analysis through federated methods.
  • Case studies: Secure data collaboration practices in financial institutions.

Challenges and Future of Federated Learning

  • Addressing technical and operational hurdles in federated learning.
  • Emerging trends and advancements in federated AI.
  • Identifying opportunities for federated learning across various industries.

Summary and Next Steps

Requirements

  • Foundational knowledge of machine learning principles.
  • Awareness of basic data privacy and security concepts.

Target 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 seeking secure AI collaboration methods.
 14 Hours

Number of participants


Price per participant

Open Training Courses require 5+ participants.

Upcoming Courses

Federated Learning for Secure AI Collaboration

2026-10-07 09:30
14 hours
Copenhagen, City Center
3472 EUR (Online)
3872 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2026-10-21 09:30
14 hours
København, Copenhagen
3472 EUR (Online)
3872 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2026-11-04 09:30
14 hours
Copenhagen, City Center
3472 EUR (Online)
3872 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2026-11-18 09:30
14 hours
København, Copenhagen
3472 EUR (Online)
3872 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2026-12-02 09:30
14 hours
Copenhagen, City Center
3472 EUR (Online)
3872 EUR (Classroom)

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