Introduction to Federated Learning

Yes. This is a free online self-paced course designed for beginners.

No. The course focuses on basic concepts and does not require coding experience.

You will learn how federated learning works, including decentralized model training, data privacy, collaborative learning, secure model updates, and real-world applications.

Students, beginners, and professionals from any background interested in AI and data privacy can join.

Yes. Learners receive an e-Certification after completing the course.

Federated learning is a machine learning approach where models are trained across multiple devices or organizations without directly sharing the original data.

Federated learning is important because it supports collaborative model training while protecting sensitive data, making it useful in areas such as healthcare, finance, mobile systems, and privacy-first AI applications.

The Introduction to Federated Learning course is designed as a 2–3 week online self-paced course.

Yes. This course is useful for learners interested in AI, data privacy, cybersecurity, secure machine learning, healthcare data, finance data, and privacy-preserving technology.

The course explains decentralized learning, data privacy, local data, shared models, model aggregation, and real-world applications in simple language without requiring prior coding or advanced machine learning knowledge. The Introduction to Federated Learning course provides a simple and structured introduction to privacy-preserving AI and decentralized machine learning. It is an ideal starting point for learners interested in secure data systems, modern AI techniques, and the future of responsible machine learning.