Course Outline

Introduction to Federated Learning

  • Overview of Federated Learning concepts
  • Decentralized model training vs. traditional centralized approaches
  • Benefits of Federated Learning in privacy and data security

Basic Federated Learning Algorithms

  • Introduction to Federated Averaging
  • Implementation of a simple Federated Learning model
  • Comparison of Federated Learning with traditional machine learning

Data Privacy and Security in Federated Learning

  • Understanding data privacy concerns in AI
  • Techniques for enhancing privacy in Federated Learning
  • Secure aggregation and data encryption methods

Practical Implementation of Federated Learning

  • Setting up a Federated Learning environment
  • Building and training a Federated Learning model
  • Deploying Federated Learning in real-world scenarios

Challenges and Limitations of Federated Learning

  • Handling non-IID data in Federated Learning
  • Communication and synchronization issues
  • Scaling Federated Learning for large networks

Case Studies and Future Trends

  • Case studies of successful Federated Learning implementations
  • Exploring the future of Federated Learning
  • Emerging trends in privacy-preserving AI

Summary and Next Steps

Requirements

  • Basic understanding of machine learning concepts
  • Experience with Python programming
  • Familiarity with data privacy principles

Audience

  • Data scientists
  • Machine learning enthusiasts
  • AI beginners
 14 Hours

Custom Corporate Training

Training solutions designed exclusively for businesses.

  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
Investment

Price per private group, online live training, starting from 3200 € + VAT*

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