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

This course, “AI for Federated Learning: Decentralized Data and Privacy-Preserving Models,” provides an in-depth exploration of Federated Learning, focusing on decentralizing machine learning to ensure data privacy. Participants will learn key privacy-preserving techniques, gain hands-on experience building secure AI models, and understand the challenges and future applications in fields like healthcare and finance. By the end, you’ll be equipped to implement privacy-first AI solutions

Provide hands-on experience with building and implementing Federated Learning models using real-world datasets.

• Learn Federated Learning and privacy-preserving techniques.

• Gain hands-on experience building decentralized AI models.

• Understand security challenges in machine learning systems.

• Apply Federated Learning in real-world privacy-sensitive applications.

📅 Day 1 – Federated Learning Basics

• AI/ML primer; centralized vs. decentralized learning; why FL for privacy & security
• FL concepts: architecture, workflow, differences from traditional ML, real examples
• Challenges: data privacy, communication/resources, convergence/synchronization
• Applications: healthcare, finance, IoT

📅 Day 2 – Privacy & Security

• Privacy tools: differential privacy, homomorphic encryption, SMPC
• Techniques: FedAvg, cryptographic protections (with hands-on demos)
• Threats & mitigations: adversarial/poisoning/Byzantine failures; TEEs
• What’s next: trends and FL’s role in decentralized AI

📅 Day 3 – Build, Use Cases & Future

• Tooling & hands-on: TFF, PySyft; train/sync/evaluate FL models
• Industry use cases: healthcare, finance, edge, IoT; case studies
• Open challenges: scalability, heterogeneity, fairness/ethics
• Future directions: edge–cloud integration, new research, ethical outlook

• AI/ML practitioners and data scientists

• Machine learning engineers

• Researchers focused on privacy and decentralized systems

• Software developers interested in implementing federated learning models

• Industry professionals in healthcare, finance, IoT, and other privacy-sensitive sectors

• Students with a basic understanding of machine learning and programming (preferably Python)

• Proficiency in implementing Federated Learning models

• Strong understanding of privacy-preserving AI techniques

• Ability to apply secure, decentralized machine learning in real-world applications

• Skills to address privacy and security challenges in AI systems

• Knowledge of emerging trends in Federated Learning and data privacy

• Preparedness for careers in AI, data privacy, and machine learning security