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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 sophisticated Federated Learning techniques

Optimization Algorithms for Federated Learning

  • Overview of optimization challenges in Federated Learning
  • Sophisticated optimization algorithms: Federated Averaging (FedAvg), Federated SGD, and others
  • Implementing and tuning optimization algorithms for extensive federated systems

Handling Non-IID Data in Federated Learning

  • Understanding non-IID data and its impact on Federated Learning
  • Strategies for managing 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 others
  • Deploying extensive Federated Learning applications

Advanced Privacy and Security Considerations

  • Privacy-preserving techniques in sophisticated Federated Learning
  • Secure aggregation and differential privacy
  • Ethical considerations in extensive Federated Learning

Case Studies and Practical Applications

  • Case study: Extensive Federated Learning in healthcare
  • Hands-on practice with sophisticated 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 methodologies
  • 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.

Provisional Upcoming Courses (Require 5+ participants)

Advanced Federated Learning Techniques

2026-08-19 09:30
21 hours
Wink Hotel
304,860,354 VND (Online)
304,860,354 VND (Classroom)

Advanced Federated Learning Techniques

2026-09-02 09:30
21 hours
Ly Thai To
304,860,354 VND (Online)
304,871,304 VND (Classroom)

Advanced Federated Learning Techniques

2026-09-16 09:30
21 hours
InterContinental Hanoi Westlake
304,860,354 VND (Online)
304,860,354 VND (Classroom)

Advanced Federated Learning Techniques

2026-09-30 09:30
21 hours
JW Marriott Hanoi
304,860,354 VND (Online)
304,860,354 VND (Classroom)

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