Federated Learning for Medical AI

Secure Collaboration, Smarter Healthcare—Federated Learning in Action

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

“Federated Learning for Medical AI” is a specialized, interdisciplinary training program designed to address one of the most critical challenges in healthcare AI: using data securely across multiple hospitals, labs, and clinics. With federated learning, sensitive medical data never leaves its source—AI models are trained collaboratively while ensuring patient privacy and regulatory compliance. This course teaches how to architect FL systems for clinical prediction, medical imaging, diagnostics, and real-world health data modeling.

Aim:

To provide participants with the theoretical understanding and practical skills to design, deploy, and evaluate federated learning (FL) systems for medical and healthcare AI applications, enabling privacy-preserving collaboration across institutions.

Program Objectives:

To introduce the principles and architectures of federated learning
To explore privacy-preserving AI systems in the context of medical data
To bridge the gap between AI capabilities and regulatory frameworks
To develop the ability to deploy federated AI systems in clinical settings

What you will learn?

Week 1: Introduction to Federated Learning and Healthcare AI

Module 1: Foundations of Federated Learning (FL)

Chapter 1.1: What is Federated Learning? Concept and Motivation
Chapter 1.2: Centralized vs. Federated vs. Distributed Learning
Chapter 1.3: Architecture of FL Systems: Clients, Servers, and Aggregators
Chapter 1.4: Key Algorithms (FedAvg, FedProx, FedBN)

Module 2: Medical Data and AI Use Cases

Chapter 2.1: AI in Healthcare: Imaging, EHRs, Genomics
Chapter 2.2: Challenges of Centralized Medical AI (Privacy, Bias, Silos)
Chapter 2.3: Why FL Suits Medical Contexts (Regulatory & Practical Needs)
Chapter 2.4: Data Heterogeneity in Medical Systems

Week 2: Building and Optimizing FL Models for Medical AI

Module 3: Data and Model Preparation

Chapter 3.1: Preprocessing Medical Data in Federated Settings
Chapter 3.2: FL with Medical Imaging (X-rays, MRIs, Histopathology)
Chapter 3.3: Text Data and EHR Modeling
Chapter 3.4: Federated Transfer Learning and Personalization

Module 4: Privacy, Security, and Ethics

Chapter 4.1: Differential Privacy and Secure Aggregation in FL
Chapter 4.2: Threat Models: Attacks on FL and Mitigation
Chapter 4.3: Regulatory Frameworks: HIPAA, GDPR, and FL
Chapter 4.4: Ethical Considerations in Medical AI Collaboration

Week 3: Real-World Applications and Deployment

Module 5: Case Studies and Open-Source Tools

Chapter 5.1: FL in Hospital Networks: COVID-19 & Cancer Detection
Chapter 5.2: Federated Clinical NLP in EHRs
Chapter 5.3: Tools and Frameworks (TensorFlow Federated, Flower, NVFlare)
Chapter 5.4: Federated Benchmark Datasets in Medicine

Module 6: Deployment, Evaluation, and Future Outlook

Chapter 6.1: System Design for Real-World FL in Clinics
Chapter 6.2: Evaluation Metrics and Cross-Site Validation
Chapter 6.3: Integrating FL into Existing Health IT Infrastructure
Chapter 6.4: Future Trends: Cross-Silo Collaboration, Multimodal FL, and Policy Impact

Intended For :

Professionals and researchers in medical AI, bioinformatics, healthcare IT
Graduate students in AI/ML, computer science, or biomedical engineering
Basic understanding of machine learning and Python recommended