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 Duration 21 hours

Course Outline

Review of Core Federated Learning Concepts

  • Summary of fundamental Federated Learning methodologies
  • Challenges in Federated Learning: communication, computation, and privacy
  • Overview of advanced Federated Learning techniques

Optimization Algorithms for Federated Learning

  • Assessment of optimization challenges within Federated Learning
  • Advanced optimization algorithms: Federated Averaging (FedAvg), Federated SGD, and related methods
  • Implementation and tuning of optimization algorithms for large-scale federated systems

Managing Non-IID Data in Federated Learning

  • Analysis of non-IID data and its effects on Federated Learning
  • Strategies for mitigating non-IID data distribution issues
  • Illustrative case studies and practical applications

Scaling Federated Learning Systems

  • Barriers to scaling Federated Learning infrastructure
  • Scalability techniques: architecture design, communication protocols, and additional measures

Advanced Privacy and Security Considerations

  • Privacy-preserving mechanisms in advanced Federated Learning
  • Secure aggregation and differential privacy frameworks
  • Ethical standards in large-scale Federated Learning operations

Case Studies and Practical Applications

  • Case study: Large-scale Federated Learning in the healthcare sector
  • Practical exercises with advanced Federated Learning scenarios
  • Execution of real-world project implementations

Future Trends in Federated Learning

  • Emerging research avenues in Federated Learning
  • Technological advancements and their influence on Federated Learning
  • Identification of future opportunities and challenges

Summary and Recommendations for Next Steps

Requirements

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

Target Audience

  • Experienced AI researchers
  • Machine learning engineers
  • Data scientists

Number of participants


Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

Upcoming Courses

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