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Federated Learning for Privacy-Preserving AI

Train ML models across decentralized data sources without sharing raw data — essential for healthcare, finance, and GDPR compliance.

6 weeks 5 lessons Planned release

Who This Is For

  • Learners building practical technical skills in this subject area.
  • Students who want applied projects rather than theory-only coverage.
  • Professionals looking for more workflow-driven learning.

How You'll Train

  • Learn the core concepts, then apply them in labs, assignments, and practical exercises.
  • Work through realistic workflows instead of isolated classroom examples.
  • Build toward a stronger project or course artifact by the end.

Technologies

PySyftTensorFlow FederatedFlowerDifferential Privacy

What You Leave With

  • Understand the core tools and workflows used in this subject area.
  • Practice applying them to realistic datasets or technical scenarios.
  • Finish with stronger evidence of practical capability.

Portfolio Outcome

A practical course artifact or project that demonstrates applied skill in this subject area.

Curriculum

Core Concepts & SetupLecture
45 min
Hands-On LabLab
90 min
QuizQuiz
15 min

Instructor

I
IODS Faculty
Curriculum delivered by practising data & ML engineers