Course Outline

Introduction to Federated Learning in Healthcare

  • Overview of Federated Learning concepts and applications
  • Challenges in applying Federated Learning to healthcare data
  • Key benefits and use cases in the healthcare sector

Ensuring Data Privacy and Security

  • Patient data privacy concerns in AI models
  • Implementing secure Federated Learning protocols
  • Ethical considerations in healthcare data management

Collaborative Model Training Across Institutions

  • Federated Learning architectures for multi-institution collaboration
  • Sharing and training AI models without data sharing
  • Overcoming challenges in cross-institutional collaborations

Real-World Case Studies

  • Case study: Federated Learning in medical imaging
  • Case study: Federated Learning for predictive analytics in healthcare
  • Practical applications and lessons learned

Implementing Federated Learning in Healthcare Settings

  • Tools and frameworks for healthcare-specific Federated Learning
  • Integrating Federated Learning with existing healthcare systems
  • Evaluating the performance and impact of Federated Learning models

Future Trends in Federated Learning for Healthcare

  • Emerging technologies and their impact on healthcare AI
  • Future directions for Federated Learning in healthcare
  • Exploring opportunities for innovation and improvement

Summary and Next Steps

Requirements

  • Experience with machine learning or AI in healthcare
  • Understanding of patient data privacy and ethical considerations
  • Proficiency in Python programming

Audience

  • Healthcare data scientists
  • Bioinformatics specialists
  • AI developers in healthcare
 21 Hours

Delivery Options

Private Group Training

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  • Training scheduled on a date of your choice
  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.

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