What You’ll Learn: Medical AI Privacy
You’ll go from understanding advanced ML models to applying federated learning techniques specifically designed to respect the privacy and security constraints of medical data.
Federated Learning Fundamentals
Learn the core principles of FL: local training, global aggregation, communication efficiency.
Privacy-Preserving Techniques
Implement differential privacy, secure multi-party computation, and homomorphic encryption.
Secure Aggregation Protocols
Master techniques like federated averaging (FedAvg) and secure aggregation to combine local models.
Medical AI Deployment
Deploy federated models in healthcare environments, ensuring compliance and robustness.
Who Is This Course For?
Ideal for experienced ML engineers, data scientists, and healthcare professionals working with sensitive medical data.
- ML engineers specializing in privacy-preserving AI
- Data scientists in healthcare/pharma
- Researchers focused on distributed ML
Hands-On Projects
Distributed Patient Risk Model
Simulate training a model to predict patient risk across multiple, non-sharing hospitals.
Secure Medical Image Classifier
Build an FL pipeline to classify medical images without centralizing the data.
Capstone
End-to-End Medical FL System
Integrate FL, privacy techniques, and deployment into a complete medical AI application.
3-Week Medical FL Syllabus
~36 hours total • Lifetime LMS access • 1:1 mentor support
Week 1: FL Fundamentals & Privacy
- Introduction to Federated Learning concepts
- Challenges of centralized vs. distributed ML
- Privacy and security in medical AI (HIPAA, GDPR)
- Basic FL algorithm (FedAvg)
Week 2: Aggregation & Communication
- Secure aggregation techniques
- Communication efficiency and compression
- Handling non-IID (non-independent) data across sites
- Differential privacy in FL
Week 3: Medical Applications & Deployment
- FL for medical image analysis
- Challenges in healthcare (data heterogeneity, compliance)
- Deploying FL models in clinical settings
- Capstone project: End-to-end medical FL application
NSTC‑Accredited Certificate
Share your verified credential on LinkedIn, resumes, and portfolios.
Frequently Asked Questions
A basic understanding of healthcare data privacy regulations (e.g., HIPAA, GDPR) is helpful. However, we cover the necessary privacy and medical concepts. Strong Python and machine learning skills are essential.
Yes! You will simulate federated learning scenarios with distributed datasets and implement secure aggregation techniques to train models without centralizing sensitive data.
