📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Introduction to Federated Learning, from foundations to a certified capstone project.
Outline
What is Federated Learning? • Why Data Privacy Matters in AI • Centralized vs Decentralized Learning • Applications of Federated Learning
Outline
Training Models Across Multiple Devices • Local Data vs Shared Models • Basic Idea of Model Aggregation • Privacy-Preserving Learning Concepts
Outline
Healthcare Data Collaboration • Mobile Devices and Personalized AI • Finance and Secure Data Systems • IoT and Edge Devices
Outline
Advantages of Federated Learning • Data Privacy and Security Benefits • Challenges in Communication and Data Diversity • Limitations of Federated Models
Outline
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
Earn government-registered certification in Introduction to Federated Learning
e-Certificate and e-Marksheet issued on successful completion.
