This course introduces learners to the integration of Federated Learning (FL) and Digital Twin (DT) technology to build secure, scalable, and privacy-preserving intelligent systems. Learners will explore how distributed machine learning can be combined with virtual replicas of physical assets to enable collaborative model training without centralized data sharing.
The course begins with foundational concepts in federated learning, privacy preservation, and digital twins, then progresses to advanced architectures, communication protocols, and optimization strategies. Through real-world case studies—such as smart manufacturing, healthcare systems, smart cities, and autonomous vehicles—learners will understand how digital twins enhance federated learning by simulating environments, validating models, and improving decision-making.
Hands-on labs and mini-projects will guide learners in designing federated learning pipelines, building digital twin models, and deploying integrated FL–DT systems using modern tools and frameworks. By the end of the course, learners will be able to architect and implement intelligent cyber-physical systems that are secure, efficient, and resilient.
Video Link for the self-learning- Teacher: Vaibhav Vasudevrao Gijare