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 Duration 21 hours

Course Outline

Revisiting Core Federated Learning Principles

  • Summary of foundational Federated Learning approaches
  • Key challenges in Federated Learning: communication, computation, and privacy
  • Introduction to advanced Federated Learning methodologies

Optimization Strategies for Federated Learning

  • Analysis of optimization hurdles in Federated Learning
  • Exploration of advanced algorithms: Federated Averaging (FedAvg), Federated SGD, and others
  • Implementation and fine-tuning of optimization algorithms for large-scale federated systems

Managing Non-IID Data in Federated Learning

  • Comprehending non-IID data and its effects on Federated Learning
  • Tactical approaches to managing non-IID data distributions
  • Insights from case studies and real-world implementations

Scaling Federated Learning Architectures

  • Addressing challenges in scaling Federated Learning systems
  • Techniques for expansion: architectural design, communication protocols, and additional strategies
  • Deployment of large-scale Federated Learning applications

Advanced Privacy and Security Protocols

  • Privacy-preserving methods in advanced Federated Learning
  • Implementation of secure aggregation and differential privacy
  • Ethical implications in large-scale Federated Learning

Case Studies and Practical Implementation

  • Case study: Large-scale Federated Learning applications in the healthcare sector
  • Hands-on exercises involving advanced Federated Learning scenarios
  • Execution of real-world projects

Future Directions in Federated Learning

  • Emerging research avenues in Federated Learning
  • Impact of technological advancements on Federated Learning
  • Exploration of future opportunities and associated challenges

Summary and Forward Planning

Requirements

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

Target Audience

  • Seasoned AI researchers
  • Machine learning engineers
  • Data scientists

Number of participants


Price per participant

Open Training Courses require 5+ participants.

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