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Course Outline

Introduction to Federated Learning

  • Definition of federated learning and its distinction from centralized learning.
  • Benefits of federated learning for secure AI collaboration.
  • Use cases and applications within sectors handling sensitive data.

Key Components of Federated Learning

  • Federated data, client roles, and model aggregation processes.
  • Communication protocols and update mechanisms.
  • Managing heterogeneity within federated environments.

Data Privacy and Security in Federated Learning

  • Principles of data minimization and privacy.
  • Techniques for securing model updates, such as differential privacy.
  • Ensuring federated learning compliance with data protection regulations.

Implementation of Federated Learning

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

Federated Learning in Healthcare

  • Challenges related to secure data sharing and privacy in healthcare.
  • Collaborative AI applications for medical research and diagnosis.
  • Case studies highlighting federated learning in medical imaging and diagnosis.

Federated Learning in Finance

  • Utilizing federated learning for secure financial modeling.
  • Fraud detection and risk analysis using federated approaches.
  • Case studies on secure data collaboration within financial institutions.

Challenges and Future Prospects of Federated Learning

  • Technical and operational hurdles in federated learning.
  • Emerging trends and advancements in federated AI.
  • Exploring opportunities for federated learning across various industries.

Summary and Next Steps

Requirements

  • Foundational knowledge of machine learning concepts.
  • Awareness of the fundamentals of data privacy and security.

Target Audience

  • Data scientists and AI researchers specializing in privacy-preserving machine learning.
  • Professionals in the healthcare and finance sectors who manage sensitive data.
  • IT and compliance managers seeking to adopt 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

2026-07-15 09:30
14 hours
Brno
3000 EUR (Online)
3000 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2026-07-29 09:30
14 hours
Ostrava
3000 EUR (Online)
3000 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2026-08-12 09:30
14 hours
Prague, Prague Castle
3000 EUR (Online)
3400 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2026-08-26 09:30
14 hours
Brno
3000 EUR (Online)
3000 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2026-09-09 09:30
14 hours
Ostrava
3000 EUR (Online)
3000 EUR (Classroom)

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