Federated Learning for Multi-Center Medical Image Diagnostics
Scale diagnostic AI across centers while staying compliant.
About This Course
This workshop explores how Federated Learning (FL) is transforming collaborative medical AI by enabling multiple hospitals and research centers to train shared diagnostic models without exchanging sensitive patient data. Participants will gain a deep understanding of privacy-preserving machine learning frameworks designed for multi-center medical imaging applications such as radiology, pathology, and oncology diagnostics.
As healthcare data is highly regulated and distributed across institutions, federated approaches provide a secure and compliant pathway to build robust, generalizable AI models while maintaining data confidentiality. This program bridges theory and practical implementation, focusing on real-world clinical challenges.
Aim
The aim of this workshop is to equip participants with the conceptual understanding and practical skills required to design and implement privacy-preserving, multi-center AI models for medical image diagnostics using Federated Learning.
Workshop Objectives
- Introduce the fundamentals of Federated Learning (FL) and its relevance in multi-center healthcare AI systems.
- Understand data privacy and regulatory frameworks (HIPAA, GDPR) applicable to distributed medical imaging.
- Analyze challenges of multi-center medical imaging data, including non-IID data distribution, domain shift, and heterogeneity.
- Explore federated optimization techniques such as FedAvg, FedProx, and secure aggregation methods.
- Develop practical skills in implementing federated pipelines using Python-based frameworks for medical image diagnostics.
- Evaluate model performance, fairness, and robustness across distributed clinical datasets.
- Design secure and scalable deployment strategies for federated AI systems in hospital environments.
- Examine real-world case studies of federated learning in radiology, pathology, and oncology imaging.
Workshop Structure
📅Day 1: Introduction to Federated Learning & Medical Imaging
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Introduction to Federated Learning: Concepts, benefits, and use in healthcare.
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Medical Image Diagnostics: Challenges in multi-center data (privacy, access).
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IoT and Data Flow: Role of IoT in medical image data collection.
Hands-on :
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Hands-on 1: Setting up a Federated Learning model for medical images.
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Hands-on 2: Preprocessing medical images for Federated Learning.
📅Day 2: Federated Learning Algorithms & Privacy
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Federated Learning Algorithms: FedAvg, FedProx for medical imaging.
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Privacy & Security: Techniques like differential privacy and encryption.
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Federated Learning in Healthcare Networks: Multi-center collaboration without data sharing.
Hands-on:
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Hands-on 3: Training a Federated Learning model using FedAvg.
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Hands-on 4: Implementing privacy techniques in Federated Learning models.
📅Day 3: Advanced Applications & Deployment
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Real-World Applications: Using Federated Learning in disease detection and classification.
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Deployment Challenges: Model convergence, communication efficiency.
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Future Trends: AI in personalized medicine and smart healthcare.
Hands-on:
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Hands-on 5: Real-time prediction with Federated Learning models.
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Hands-on 6: Deploying Federated Learning models for collaborative diagnostics.
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
03/09/2026
IST 04:30 PM IST
Workshop Dates
03/09/2026 – 03/11/2026
IST 05:30PM IST
Workshop Outcomes
- Explain the principles and architecture of Federated Learning and its role in distributed healthcare AI systems.
- Design privacy-preserving multi-center training pipelines for medical image diagnostics without sharing raw patient data.
- Implement federated learning algorithms (e.g., FedAvg, FedProx) using practical tools and frameworks.
- Handle non-IID and heterogeneous medical imaging datasets across different hospitals or research centers.
- Apply secure aggregation and data protection strategies aligned with healthcare compliance standards.
- Evaluate and validate federated diagnostic models using appropriate performance, robustness, and fairness metrics.
- Assess deployment challenges and propose scalable solutions for real-world clinical environments.
- Develop collaborative AI strategies that enhance diagnostic accuracy while maintaining ethical and regulatory standards.
Fee Structure
Student
₹2499 | $75
Ph.D. Scholar / Researcher
₹3499 | $85
Academician / Faculty
₹4499 | $95
Industry Professional
₹6499 | $115
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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