Federated Learning for Medical AI
Live Registration
BATCH #24
03Seats Left
92% Booked
02
Days
:
00
Hrs
:
00
Min
:
00
Sec
Course Overview
Table of Contents
Program Syllabus
Module 1
About
“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.
Module 2
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.
Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Limited SeatsClosing Soon
Federated Learning for Medical AI
Professional Certification Program
🎥
FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
Module 3
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
Module 4
Program Structure
Module 5
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 6
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
Module 7
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 8
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
Module 9
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 10
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
Module 11
Video Module TOC
~Video content aligned with weekly modules
Module 12
Week 1 Videos
-
Introduction to Federated Learning
-
Centralized vs. Federated Learning
-
Core Components of FL Systems
-
Key Algorithms: FedAvg, FedProx, FedBN
-
Overview of AI in Healthcare
-
Data Privacy Challenges in Centralized Medical AI
-
Why Federated Learning Fits Medical Applications
-
Medical Data Characteristics and Cross-Silo Heterogeneity
-
Week 1 Recap and Quiz Preparation
Module 13
Week 2 Videos
-
Preprocessing Pipelines for Federated Medical Data
-
FL for Medical Imaging (X-ray, MRI, Histology Use Cases)
-
Modeling EHR and Clinical Text in FL Environments
-
Personalized Federated Learning in Healthcare
-
Applying Differential Privacy to FL
-
Secure Aggregation Techniques for Sensitive Data
-
Attack Vectors in FL and Mitigation Strategies
-
Legal and Ethical Compliance (HIPAA, GDPR in FL)
-
Week 2 Project Brief: Multi-site Diagnostic Model Build
Module 14
Week 3 Videos
-
Hospital Network Case Study: FL for COVID-19 CT Scans
-
Clinical NLP Case Study: Federated EHR Summarization
-
Tool Demo: TensorFlow Federated and Flower Framework
-
Open Medical FL Datasets and Benchmarks
-
Architecting Real-World FL Systems in Healthcare
-
Validation Metrics and Multi-Institution Evaluation
-
Interoperability with Health IT and PACS Systems
-
Capstone Project Walkthrough
-
Future of FL in Global Healthcare Collaboration
Module 15
Live Lecture Module
Interactive sessions to explore real-world FL challenges and breakthroughs in medical AI
Module 16
Lecture 1 (Week 1)
Title: Federated Learning in Medicine: Why Privacy-First AI Matters
Duration: 60 minutes
Focus: Introduction to federated learning and its unique relevance to healthcare systems
Guest: Clinical Data Scientist or AI/Health Informatics Professor
Interactive: Live Q&A on patient data privacy + case-matching exercise across institutions
Module 17
Lecture 2 (Week 2)
Title: Building Medical AI Collaboratively: Tools, Models, and Site Challenges
Duration: 75 minutes
Focus: Technical workflow of FL in medical imaging and EHR, with model tuning tips
Guest: Researcher from a federated healthcare study or FL framework contributor
Interactive: Hands-on walkthrough of FL training loop using real data simulation
Module 18
Lecture 3 (Week 3)
Title: From Prototype to Practice: Deploying FL Systems in Hospitals
Duration: 90 minutes
Focus: Deployment challenges, validation protocols, and regulatory integration
Guest Panel: Hospital CIO + Medical AI Engineer + FL Policy Advisor
Interactive: Capstone feedback session + group review of deployment scenarios
Module 19
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
Module 20
Program Outcomes
Upon completion of the course, participants will:
-
Build and simulate federated learning models for healthcare use-cases
-
Apply secure and privacy-preserving techniques to real-world clinical datasets
-
Evaluate FL model performance in heterogeneous environments
-
Understand and mitigate risks in distributed AI model development
Module 22
Fee Structure
Fee: INR 21499 USD 249
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of CurrenciesModule 23
FOR QUERIES, FEEDBACK OR ASSISTANCE
Module 24
Key Takeaways
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Module 25
Future Career Prospects
Learners completing this course will be prepared for roles such as:
-
Federated Learning Engineer (Healthcare)
-
AI Researcher – Medical Privacy and Security
-
Biomedical Data Scientist (FL applications)
-
AI Solution Architect – HealthTech Industry
Module 26
Job Opportunities
-
Medical AI Engineer (Federated Systems)
-
Clinical AI Architect (Hospitals, Health Networks)
-
Digital Health Research Scientist
-
AI Product Manager – Privacy-Preserving Tools
-
Healthcare Data Collaborator (R&D)
-
Bioinformatics Platform Developer (FL-integrated)
Module 27
Enter the Hall of Fame!
Take your research to the next level!
Publication Opportunity
Potentially earn a place in our coveted Hall of Fame.
Centre of Excellence
Join the esteemed Centre of Excellence.
Networking and Learning
Network with industry leaders, access ongoing learning opportunities.
Hall of Fame
Get your groundbreaking work considered for publication in a prestigious Open Access Journal (worth ₹20,000/USD 1,000).
Achieve excellence and solidify your reputation among the elite!
×
Module 28
![]()
![]()
![]()
![]()
Related Courses
Digital Twins: Predictive …
AI in Sound Modification
AI, Biopolymers, and Smart …
AI-Powered Drug Discovery with …
Module 29
Machine Learning for Optimizing Lipid Nanoparticles (LNPs) in mRNA & Gene Delivery
Machine Learning for Optimizing Lipid Nanoparticles (LNPs) in mRNA & Gene Delivery
Surveillance and Data Analytics of Antimicrobial Resistance (AMR) in Public Health
View All Feedbacks
Recent Feedbacks In Other Workshops
none
Instant Access
Need Guidance?
Not sure if this course is right for you? Schedule a free 15-minute consultation with our academic advisors.