AI for Federated Learning: Decentralized Data and Privacy-Preserving Models

Introduction to the Course

Course Objectives

• Understand the basics of federated learning and its use cases in privacy-conscious domains.
• Learn how decentralized data processing enables organizations to jointly work on machine learning models without exchanging sensitive information.
• Get practical experience with federated learning algorithms and libraries such as TensorFlow Federated and PySyft.
• Acquire skills to design and implement privacy-preserving models using techniques such as differential privacy and secure multi-party computation (SMPC).
• Discover the practical applications of federated learning in industries such as healthcare, finance, and IoT.
• Develop skills to design and deploy privacy-preserving AI models that are GDPR compliant

What Will You Learn (Modules)

Module 1 – Federated Learning Basics

• AI/ML Primer: Learn the difference between centralized and decentralized learning, and understand why Federated Learning is crucial for privacy and security.
• FL Concepts: Explore the architecture and workflow of Federated Learning, with real-world examples of its application.
• Challenges: Dive into the challenges of Federated Learning, including data privacy concerns, communication/resource limitations, and issues around convergence and synchronization.
• Applications: See how Federated Learning is used in healthcare, finance, and IoT environments.

 Module 2 – Privacy & Security

• Privacy Tools: Get hands-on with privacy-preserving tools like differential privacy, homomorphic encryption, and secure multi-party computation (SMPC).
• Techniques: Learn FL techniques such as FedAvg and cryptographic protections, with live demonstrations.
• Threats & Mitigations: Understand the security risks in Federated Learning (e.g., adversarial attacks, poisoning, and Byzantine failures) and how to protect against them using advanced techniques like Trusted Execution Environments (TEEs).
• What’s Next: Explore the latest trends in Federated Learning and its role in the future of decentralized AI.

 Module 3 – Build, Use Cases & Future

• Tooling & Hands-On: Get practical experience with tools like TensorFlow Federated (TFF) and PySyft to train, synchronize, and evaluate Federated Learning models.
• Industry Use Cases: Discover how Federated Learning is applied in healthcare, finance, edge computing, and IoT, with real-world case studies.
• Open Challenges: Discuss scalability, heterogeneity, and fairness issues in Federated Learning.
• Future Directions: Look into the future of Federated Learning, including edge-cloud integration, ongoing research, and the ethical considerations surrounding its growth.

Who Should Take This Course?

This course is ideal for:

• Data scientists and machine learning engineers looking to work with privacy-preserving AI techniques
• Privacy professionals interested in applying differential privacy and federated learning for secure data handling
• Healthcare professionals and financial analysts who work with sensitive data and need to comply with data protection regulations
• IoT developers interested in federated learning for smart devices and edge computing
• Students in computer science, data science, or AI programs who want to specialize in decentralized machine learning and privacy preservation

Job Opportunities

After completing this course, learners can pursue roles such as:

• Federated Learning Engineer
• Privacy-Preserving Machine Learning Specialist
• Data Privacy Consultant (AI & ML)
• Secure AI Developer

Why Learn With Nanoschool?

At NanoSchool, we focus on career-relevant learning that builds real capability—not just theory.

• Expert-led training: Learn from instructors with real-world experience in applying skills to industry and research problems.
• Practical & hands-on approach: Build skills through guided activities, templates, and task-based learning you can apply immediately.
• Industry-aligned curriculum: Course content is designed around current tools, workflows, and expectations from employers.
• Portfolio-ready outcomes: Create outputs you can showcase in interviews, academic profiles, proposals, or real work.
• Learner support: Get structured guidance, clear learning paths, and support to stay consistent and finish strong.

Key outcomes of the course

Upon completion, learners will be able to:

• In-depth knowledge of federated learning and its privacy-preserving machine learning capabilities
• Hands-on experience with differential privacy and SMPC methods in AI applications
• Comfort level with TensorFlow Federated and PySyft frameworks for implementing federated learning models
• Skill to apply privacy-preserving AI to practical applications in healthcare, finance, and IoT domains
• Capstone project with a portfolio-ready implementation of your knowledge in federated learning and privacy-preserving models