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Course Outline

Review of Core Federated Learning Concepts

  • Recap of fundamental Federated Learning methodologies.
  • Challenges in Federated Learning: communication, computation, and privacy.
  • Introduction to advanced Federated Learning techniques.

Optimization Algorithms for Federated Learning

  • Overview of optimization challenges in Federated Learning.
  • Advanced optimization algorithms: Federated Averaging (FedAvg), Federated SGD, and others.
  • Implementing and tuning optimization algorithms for large-scale federated systems.

Handling Non-IID Data in Federated Learning

  • Understanding non-IID data and its impact on Federated Learning.
  • Strategies for handling non-IID data distributions.
  • Case studies and real-world applications.

Scaling Federated Learning Systems

  • Challenges in scaling Federated Learning systems.
  • Techniques for scaling up: architecture design, communication protocols, and more.
  • Deploying large-scale Federated Learning applications.

Advanced Privacy and Security Considerations

  • Privacy-preserving techniques in advanced Federated Learning.
  • Secure aggregation and differential privacy.
  • Ethical considerations in large-scale Federated Learning.

Case Studies and Practical Applications

  • Case study: Large-scale Federated Learning in healthcare.
  • Hands-on practice with advanced Federated Learning scenarios.
  • Real-world project implementation.

Future Trends in Federated Learning

  • Emerging research directions in Federated Learning.
  • Technological advancements and their impact on Federated Learning.
  • Exploring future opportunities and challenges.

Summary and Next Steps

Requirements

  • Experience with machine learning and deep learning techniques.
  • Understanding of fundamental Federated Learning concepts.
  • Proficiency in Python programming.

Audience

  • Experienced AI researchers.
  • Machine learning engineers.
  • Data scientists.
 21 Hours

Number of participants


Price per participant

Open Training Courses require 5+ participants.

Upcoming Courses

Advanced Federated Learning Techniques

2026-07-20 09:30
21 hours
Kuala Lumpur KL Sentral
21661 MYR (Online)
22636 MYR (Classroom)

Advanced Federated Learning Techniques

2026-08-03 09:30
21 hours
Bayan Lepas, iDEAL
21661 MYR (Online)
22396 MYR (Classroom)

Advanced Federated Learning Techniques

2026-08-17 09:30
21 hours
Kuala Lumpur KL Sentral
21661 MYR (Online)
22636 MYR (Classroom)

Advanced Federated Learning Techniques

2026-08-31 09:30
21 hours
Bayan Lepas, iDEAL
21661 MYR (Online)
22396 MYR (Classroom)

Advanced Federated Learning Techniques

2026-09-14 09:30
21 hours
Kuala Lumpur KL Sentral
21661 MYR (Online)
22636 MYR (Classroom)

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