AI for Federated Learning: Decentralized Data and Privacy-Preserving Models
Empowering Privacy, Enhancing Collaboration: AI for Decentralized and Secure Learning
About This Course
This course, “AI for Federated Learning: Decentralized Data and Privacy-Preserving Models,” provides an in-depth exploration of Federated Learning, focusing on decentralizing machine learning to ensure data privacy. Participants will learn key privacy-preserving techniques, gain hands-on experience building secure AI models, and understand the challenges and future applications in fields like healthcare and finance. By the end, you’ll be equipped to implement privacy-first AI solutions
Aim
Provide hands-on experience with building and implementing Federated Learning models using real-world datasets.
Workshop Objectives
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Learn Federated Learning and privacy-preserving techniques.
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Gain hands-on experience building decentralized AI models.
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Understand security challenges in machine learning systems.
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Apply Federated Learning in real-world privacy-sensitive applications.
Workshop Structure
📅 Day 1 – Federated Learning Basics
- AI/ML primer; centralized vs. decentralized learning; why FL for privacy & security
- FL concepts: architecture, workflow, differences from traditional ML, real examples
- Challenges: data privacy, communication/resources, convergence/synchronization
- Applications: healthcare, finance, IoT
📅 Day 2 – Privacy & Security
- Privacy tools: differential privacy, homomorphic encryption, SMPC
- Techniques: FedAvg, cryptographic protections (with hands-on demos)
- Threats & mitigations: adversarial/poisoning/Byzantine failures; TEEs
- What’s next: trends and FL’s role in decentralized AI
📅 Day 3 – Build, Use Cases & Future
- Tooling & hands-on: TFF, PySyft; train/sync/evaluate FL models
- Industry use cases: healthcare, finance, edge, IoT; case studies
- Open challenges: scalability, heterogeneity, fairness/ethics
- Future directions: edge–cloud integration, new research, ethical outlook
Who Should Enrol?
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AI/ML practitioners and data scientists
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Machine learning engineers
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Researchers focused on privacy and decentralized systems
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Software developers interested in implementing federated learning models
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Industry professionals in healthcare, finance, IoT, and other privacy-sensitive sectors
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Students with a basic understanding of machine learning and programming (preferably Python)
Important Dates
Registration Ends
10/28/2025
IST 4:30 PM
Workshop Dates
10/28/2025 – 10/30/2025
IST 5:30 PM
Workshop Outcomes
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Proficiency in implementing Federated Learning models
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Strong understanding of privacy-preserving AI techniques
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Ability to apply secure, decentralized machine learning in real-world applications
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Skills to address privacy and security challenges in AI systems
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Knowledge of emerging trends in Federated Learning and data privacy
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Preparedness for careers in AI, data privacy, and machine learning security
Fee Structure
Student
₹1999 | $55
Ph.D. Scholar / Researcher
₹2999 | $65
Academician / Faculty
₹3999 | $75
Industry Professional
₹5999 | $95
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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