| Attribute | Detail |
|---|---|
| Format | Online, self-paced course |
| Level | Basic / Beginner |
| Duration | 2–3 Weeks |
| Certification | e-Certification |
| Fee | ₹199 / $20 |
| Tools | Federated Learning Data Privacy Decentralized AI Machine Learning Edge Computing |
About the Introduction to Federated Learning Course
The Introduction to Federated Learning course is a free, beginner-friendly self-paced program designed to help learners understand how machine learning can be performed without directly sharing data.
The course explains how federated learning enables multiple devices or organizations to collaboratively train models while keeping data private. Learners will explore concepts such as decentralized learning, data privacy, secure model updates, and real-world applications in sensitive domains like healthcare and finance.
Program Highlights
• Free beginner-level federated learning course
• Online self-paced learning format
• Simple explanation of decentralized machine learning
• Covers data privacy and collaborative model training basics
• Real-world examples from healthcare, finance, and mobile systems
• Suitable for students and non-technical learners
• e-Certification upon successful completion
Course Curriculum
Module 1: Introduction to Federated Learning
- What is Federated Learning?
- Why Data Privacy Matters in AI
- Centralized vs Decentralized Learning
- Applications of Federated Learning
Module 2: How Federated Learning Works
- Training Models Across Multiple Devices
- Local Data vs Shared Models
- Basic Idea of Model Aggregation
- Privacy-Preserving Learning Concepts
Module 3: Applications of Federated Learning
- Healthcare Data Collaboration
- Mobile Devices and Personalized AI
- Finance and Secure Data Systems
- IoT and Edge Devices
Module 4: Benefits and Challenges
- Advantages of Federated Learning
- Data Privacy and Security Benefits
- Challenges in Communication and Data Diversity
- Limitations of Federated Models
Module 5: Future Scope and Next Steps
- Federated Learning in AI and Edge Computing
- Emerging Trends in Privacy-Preserving AI
- Career Opportunities in AI and Data Privacy
- Mini Learning Activity / Concept-Based Practice
Tools, Techniques, or Platforms Covered
Federated Learning Data Privacy Decentralized AI Machine Learning Edge Computing
Real-World Applications
- Training AI models without sharing sensitive data
- Protecting medical records while enabling AI research
- Personalizing mobile apps without central data storage
- Enhancing financial systems with secure data handling
- Supporting privacy-first AI systems in modern technology
Who Should Attend & Prerequisites
- This course is suitable for students, beginners, freshers, and professionals interested in AI, data privacy, and secure machine learning.
- It is also useful for learners from data science, cybersecurity, healthcare, finance, and technology backgrounds.
Certification
