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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
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Day 1: Introduction to Federated Learning and Privacy-Preserving Models
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Introduction to AI and Machine Learning
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Overview of AI and ML fundamentals
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Differences between centralized and decentralized learning
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Why Federated Learning is essential for privacy and security?
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Federated Learning: Concepts and Architecture
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What is Federated Learning?
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The architecture and workflow of Federated Learning
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Federated Learning vs traditional machine learning models
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Examples and real-world use cases
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Challenges in Federated Learning
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Data privacy issues and decentralized data
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Communication efficiency and resource constraints
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Model convergence and synchronization
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Exploring Federated Learning Applications
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Real-world implementations: Healthcare, Finance, IoT, and more
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Day 2: Privacy-Preserving Techniques and Security in Federated Learning
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Data Privacy in Federated Learning
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Importance of privacy in machine learning models
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Introduction to differential privacy
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Homomorphic encryption and its role in Federated Learning
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Secure multi-party computation (SMPC) for secure data handling
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Privacy-Preserving Techniques
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Differential privacy in Federated Learning
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Federated Averaging and its impact on privacy
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Cryptographic methods for privacy protection
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Hands-on examples of privacy techniques in action
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Security Challenges in Federated Learning
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Adversarial attacks, model poisoning, and Byzantine failures
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Threats and vulnerabilities in decentralized systems
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Solutions to mitigate security risks
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Trusted Execution Environments (TEEs) for secure computing
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Future of Privacy-Preserving Federated Learning
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Emerging trends in privacy and security for AI
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The role of Federated Learning in the future of decentralized AI
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Day 3: Implementation, Use Cases, and Future Directions
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Building Federated Learning Models
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Introduction to frameworks like TensorFlow Federated, PySyft, etc.
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Hands-on workshop: Building a Federated Learning model from scratch
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Training models on decentralized data and synchronizing updates
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Evaluating model performance in a Federated Learning setup
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Exploring Real-World Use Cases
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Industry applications: Healthcare, finance, edge computing, and IoT
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How Federated Learning is applied to real-world data privacy challenges
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Case study walkthroughs
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Open Challenges in Federated Learning
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Scalability and resource management issues
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Handling data heterogeneity in decentralized networks
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Addressing fairness and ethical considerations in Federated Learning
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The Future of Federated Learning
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New research areas and evolving trends
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Integrating Federated Learning with edge and cloud computing
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Ethical considerations and the future of privacy in AI
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Conclusion & Wrap-up
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Key takeaways and final thoughts
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Q&A session
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Feedback and next steps for continued learning
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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
10/15/2025
IST 4:30 PM
Workshop Dates
10/15/2025 – 10/17/2025
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 Fee
₹1999 | $55
Ph.D. Scholar / Researcher Fee
₹2999 | $65
Academician / Faculty Fee
₹3999 | $75
Industry Professional Fee
₹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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