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 Duration 14 hours

Course Outline

Introduction to Federated Learning

  • Defining federated learning and its distinctions from centralized learning.
  • Key advantages of federated learning for secure AI collaboration.
  • ‌Applications and use cases in sectors with sensitive data.

Core Components of Federated Learning

  • Understanding federated data, clients, and model aggregation.
  • Communication protocols and update mechanisms.
  • Managing heterogeneity within federated environments.

Data Privacy and Security in Federated Learning

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

Implementing Federated Learning

  • Configuring a federated learning environment.
  • Conducting distributed model training using federated frameworks.
  • Evaluating performance and accuracy considerations.

Federated Learning in Healthcare

  • ‌Addressing secure data sharing and privacy concerns in healthcare.
  • Leveraging collaborative AI for medical research and diagnosis.
  • ‌Case studies: Applying federated learning to medical imaging and diagnosis.

Federated Learning in Finance

  • ‌Utilizing federated learning for secure financial modeling.
  • ‌Employing federated approaches for fraud detection and risk analysis.
  • ‌Case studies: Secure data collaboration within financial institutions.

Challenges and Future of Federated Learning

  • ‌Navigating technical and operational challenges in federated learning.
  • ‌Identifying future trends and advancements in federated AI.
  • ‌Exploring cross-industry opportunities for federated learning.

Summary and Next Steps

Requirements

  • Foundational knowledge of machine learning principles
  • Basic familiarity with 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 datasets
  • IT and compliance managers seeking to implement secure AI collaboration methods

Number of participants


Price per participant

Open Training Courses require 5+ participants.

Upcoming Courses

Federated Learning for Secure AI Collaboration

2026-11-13 09:30
14 hours
Riga, Old Town
3000 EUR (Online)
3400 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2026-11-27 09:30
14 hours
Riga, Old Town
3000 EUR (Online)
3400 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2026-12-11 09:30
14 hours
Riga, Old Town
3000 EUR (Online)
3400 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2026-12-25 09:30
14 hours
Riga, Old Town
3000 EUR (Online)
3400 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2027-01-08 09:30
14 hours
Riga, Old Town
3000 EUR (Online)
3400 EUR (Classroom)

Federated Learning for Secure AI Collaboration

2027-01-22 09:30
14 hours
Riga, Old Town
3000 EUR (Online)
3400 EUR (Classroom)

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