AI for Federated Learning: Decentralized Data and Privacy-Preserving Models
Empowering Privacy, Enhancing Collaboration: AI for Decentralized and Secure Learning
Skills you will gain:
About Program:
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
Aim: Provide hands-on experience with building and implementing Federated Learning models using real-world datasets.
Program Objectives:
-
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.
What you will learn?
📅 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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
-
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)
Career Supporting Skills
Program Outcomes
-
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