Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Federated Learning: Decentralized Data & Privacy-Preserving ML addresses a central tension in modern AI: models need data, but data increasingly cannot be centralised for privacy or regulatory reasons. You learn how federated learning trains a shared model across many devices or institutions without moving their raw data, the aggregation and communication methods behind it, and the privacy techniques (differential privacy, secure aggregation) that harden it. The course covers the real challenges — heterogeneity, communication cost and security. You finish able to reason about a federated-learning system. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers federated learning — training machine-learning models across decentralised data sources while preserving privacy, and its systems and security challenges.
📋 Course Objectives
1. Explain how federated learning works.
2. Apply model aggregation and communication methods.
3. Add privacy with differential privacy and secure aggregation.
4. Handle data heterogeneity across clients.
5. Address federated security and robustness.
👥 Who Should Enroll?
• ML engineers and researchers
• Privacy and security professionals
• Healthcare and finance data teams
• Students of privacy-preserving ML
🚀 Key Learning Outcomes
• An understanding of federated learning.
• A privacy-preserving ML perspective.
• A foundation in decentralised AI.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Introduction to Federated Learning
Overview of Federated Learning and Its Importance • Why Decentralized Data Matters in Modern AI Systems • Difference Between Centralized and Federated Learning Approaches • Applications of Federated Learning in Privacy-Sensitive Industries
Fundamentals of Artificial Intelligence
Introduction to Artificial Intelligence in Data-Driven Systems • AI Model Training, Prediction, and Decision-Making Concepts • Role of Data Quality, Model Performance, and Generalization • Challenges of AI Development When Data Cannot Be Centralized
Decentralized Data Environments
Understanding Decentralized Data Systems • Data Distribution Across Devices, Institutions, and Networks • Privacy, Compliance, and Ownership Challenges in Distributed Data • Designing AI Workflows for Decentralized Settings
Federated Learning Architecture
Core Components of Federated Learning Systems • Local Model Training and Global Model Aggregation • Communication Between Clients and Central Coordination Systems • Federated Learning Workflow from Initialization to Model Update
Privacy-Preserving Learning Techniques
Importance of Privacy in Federated Learning • Reducing Exposure of Sensitive Data During AI Training • Secure Model Updates and Privacy-Aware Collaboration • Balancing Model Utility, Privacy, and System Efficiency
Challenges in Federated AI Systems
Data Heterogeneity and Non-Uniform Data Distribution • Communication Costs and System Scalability • Model Accuracy, Reliability, and Fairness Concerns • Security Risks in Federated and Decentralized Learning Environments
Applications of Federated Learning
Federated Learning in Healthcare and Medical Research • Applications in Banking, Finance, Insurance, and Fraud Detection • Federated AI for Mobile Devices, IoT, and Smart Systems • Enterprise Use Cases for Collaborative AI Without Raw Data Sharing
Case Studies and Future Opportunities
Case Studies in Federated Learning and Privacy-Preserving AI • Ethical, Legal, and Governance Considerations • Future Opportunities in Decentralized AI and Secure Collaboration • Final Applied Review on Federated Learning System Design
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Artificial Intelligence |
| Covered Tool / Platform | Decentralized |
| Covered Tool / Platform | Federated |
| Covered Tool / Platform | Learning |
| Covered Tool / Platform | Federated Learning |
| Covered Tool / Platform | Privacy-Preserving AI |
| Covered Tool / Platform | Decentralized Data |
| Covered Tool / Platform | Distributed Learning |
| Covered Tool / Platform | Secure Collaboration |
| Covered Tool / Platform | Responsible AI |
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