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Course Outline

Introduction to Federated Learning

  • Defining Federated Learning and its distinctions from centralized learning
  • Strategic advantages of Federated Learning for secure AI collaboration
  • Practical use cases and applications in sectors with sensitive data

Core Components of Federated Learning

  • Federated data structures, clients, and model aggregation processes
  • Communication protocols and model update mechanisms
  • Managing heterogeneity within Federated Learning environments

Data Privacy and Security in Federated Learning

  • Data minimization strategies and core privacy principles
  • 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 Learning frameworks
  • Addressing performance and accuracy considerations

Federated Learning in Healthcare

  • Secure data sharing mechanisms and privacy considerations in healthcare
  • Collaborative AI applications in medical research and diagnosis
  • Case studies: Federated Learning in medical imaging and diagnostic processes

Federated Learning in Finance

  • Leveraging Federated Learning for secure financial modeling
  • Applying Federated Learning to fraud detection and risk analysis
  • Case studies on secure data collaboration within financial institutions

Challenges and the Future of Federated Learning

  • Navigating technical and operational challenges in Federated Learning
  • Emerging trends and advancements in Federated AI
  • Identifying opportunities for Federated Learning across various industries

Summary and Recommended Next Steps

Requirements

  • Foundational knowledge of machine learning concepts
  • Working familiarity with 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 methodologies
 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
Empire Tower
63214 THB (Online)
63214 THB (Classroom)

Federated Learning for Secure AI Collaboration

2026-10-21 09:30
14 hours
Empire Tower
63214 THB (Online)
63214 THB (Classroom)

Federated Learning for Secure AI Collaboration

2026-11-04 09:30
14 hours
Empire Tower
63214 THB (Online)
63214 THB (Classroom)

Federated Learning for Secure AI Collaboration

2026-11-18 09:30
14 hours
Empire Tower
63214 THB (Online)
63214 THB (Classroom)

Federated Learning for Secure AI Collaboration

2026-12-02 09:30
14 hours
Empire Tower
63214 THB (Online)
63214 THB (Classroom)

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