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 Duration 21 hours

Course Outline

Revisiting Core Federated Learning Principles

  • Review of foundational Federated Learning approaches
  • Key challenges: communication efficiency, computational load, and privacy
  • Overview of advanced Federated Learning techniques

Optimization Strategies for Federated Learning

  • Analysis of optimization hurdles specific to Federated Learning
  • Exploration of advanced algorithms, including Federated Averaging (FedAvg) and Federated SGD
  • Practical implementation and tuning of optimization algorithms for extensive federated architectures

Managing Non-IID Data in Federated Learning

  • Examining the nature of non-IID data and its effects on model training
  • Strategies for mitigating non-IID data distribution issues
  • Insights from case studies and real-world applications

Scaling Federated Learning Architectures

  • Identifying barriers to scaling Federated Learning systems
  • Advanced techniques for expansion, such as architecture design and optimized communication protocols
  • Deployment strategies for large-scale Federated Learning applications

Advanced Privacy and Security Frameworks

  • Implementation of privacy-preserving methods in advanced contexts
  • Utilization of secure aggregation and differential privacy
  • Ethical implications and considerations in large-scale deployments

Practical Applications and Case Studies

  • Case study analysis: Large-scale Federated Learning in the healthcare sector
  • Hands-on workshops involving advanced Federated Learning scenarios
  • Execution of real-world project implementations

Emerging Trends in Federated Learning

  • Current research directions and innovations
  • The impact of technological advancements on Federated Learning
  • Assessment of future opportunities and potential challenges

Conclusion and Path Forward

Requirements

  • Practical experience with machine learning and deep learning methodologies
  • A solid grasp of fundamental Federated Learning principles
  • Strong proficiency in Python programming

Intended Audience

  • Senior AI researchers
  • Machine learning engineers
  • Data scientists

Number of participants


Price per participant (excl. VAT)

Open Training Courses require 5+ participants.

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