AED
UAD Dirham
Code: AED
Mentor Based
Federated Learning for Medical AI
Secure Collaboration, Smarter Healthcare—Federated Learning in Action
Early access to the e-LMS platform is included
About This Course
“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
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To introduce the principles and architectures of federated learning
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To explore privacy-preserving AI systems in the context of medical data
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To bridge the gap between AI capabilities and regulatory frameworks
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To develop the ability to deploy federated AI systems in clinical settings
Program Structure
Week 1: Introduction to Federated Learning and Healthcare AI
Module 1: Foundations of Federated Learning (FL)
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Chapter 1.1: What is Federated Learning? Concept and Motivation
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Chapter 1.2: Centralized vs. Federated vs. Distributed Learning
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Chapter 1.3: Architecture of FL Systems: Clients, Servers, and Aggregators
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Chapter 1.4: Key Algorithms (FedAvg, FedProx, FedBN)
Module 2: Medical Data and AI Use Cases
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Chapter 2.1: AI in Healthcare: Imaging, EHRs, Genomics
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Chapter 2.2: Challenges of Centralized Medical AI (Privacy, Bias, Silos)
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Chapter 2.3: Why FL Suits Medical Contexts (Regulatory & Practical Needs)
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Chapter 2.4: Data Heterogeneity in Medical Systems
Week 2: Building and Optimizing FL Models for Medical AI
Module 3: Data and Model Preparation
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Chapter 3.1: Preprocessing Medical Data in Federated Settings
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Chapter 3.2: FL with Medical Imaging (X-rays, MRIs, Histopathology)
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Chapter 3.3: Text Data and EHR Modeling
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Chapter 3.4: Federated Transfer Learning and Personalization
Module 4: Privacy, Security, and Ethics
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Chapter 4.1: Differential Privacy and Secure Aggregation in FL
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Chapter 4.2: Threat Models: Attacks on FL and Mitigation
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Chapter 4.3: Regulatory Frameworks: HIPAA, GDPR, and FL
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Chapter 4.4: Ethical Considerations in Medical AI Collaboration
Week 3: Real-World Applications and Deployment
Module 5: Case Studies and Open-Source Tools
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Chapter 5.1: FL in Hospital Networks: COVID-19 & Cancer Detection
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Chapter 5.2: Federated Clinical NLP in EHRs
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Chapter 5.3: Tools and Frameworks (TensorFlow Federated, Flower, NVFlare)
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Chapter 5.4: Federated Benchmark Datasets in Medicine
Module 6: Deployment, Evaluation, and Future Outlook
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Chapter 6.1: System Design for Real-World FL in Clinics
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Chapter 6.2: Evaluation Metrics and Cross-Site Validation
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Chapter 6.3: Integrating FL into Existing Health IT Infrastructure
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Chapter 6.4: Future Trends: Cross-Silo Collaboration, Multimodal FL, and Policy Impact
Who Should Enrol?
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Professionals and researchers in medical AI, bioinformatics, healthcare IT
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Graduate students in AI/ML, computer science, or biomedical engineering
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Basic understanding of machine learning and Python recommended
Program Outcomes
Upon completion of the course, participants will:
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Build and simulate federated learning models for healthcare use-cases
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Apply secure and privacy-preserving techniques to real-world clinical datasets
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Evaluate FL model performance in heterogeneous environments
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Understand and mitigate risks in distributed AI model development
Fee Structure
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- One-on-one project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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