Course Outline

Review of Core Federated Learning Concepts for Government

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

Optimization Algorithms for Federated Learning

  • Overview of optimization challenges in Federated Learning for government use cases
  • Advanced optimization algorithms: Federated Averaging (FedAvg), Federated Stochastic Gradient Descent (SGD), and more
  • Implementing and tuning optimization algorithms for large-scale federated systems in public sector environments

Handling Non-IID Data in Federated Learning

  • Understanding non-IID data and its impact on Federated Learning for government projects
  • Strategies for managing non-IID data distributions in federal initiatives
  • Case studies and real-world applications within public sector workflows

Scaling Federated Learning Systems for Government

  • Challenges in scaling Federated Learning systems for government operations
  • Techniques for scaling up: architecture design, communication protocols, and more
  • Deploying large-scale Federated Learning applications in public sector contexts

Advanced Privacy and Security Considerations for Government

  • Privacy-preserving techniques in advanced Federated Learning for government agencies
  • Secure aggregation and differential privacy methods tailored for federal use
  • Ethical considerations in large-scale Federated Learning within the public sector

Case Studies and Practical Applications for Government

  • Case study: Large-scale Federated Learning in healthcare for government agencies
  • Hands-on practice with advanced Federated Learning scenarios for federal projects
  • Real-world project implementation within public sector environments

Future Trends in Federated Learning for Government

  • Emerging research directions in Federated Learning for government applications
  • Technological advancements and their impact on Federated Learning for federal use
  • Exploring future opportunities and challenges for government agencies

Summary and Next Steps for Government

Requirements

  • Experience with machine learning and deep learning methodologies
  • Familiarity with fundamental Federated Learning principles
  • Proficiency in Python programming

Audience for Government

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

Number of participants


Price per participant

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

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