Lilly Tech Systems
Learn Federated Learning
Train machine learning models across decentralized data sources without sharing raw data. Master privacy-preserving AI, FedAvg, differential privacy, and federated frameworks — all for free.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Federated Learning? Why train models without centralizing data?
2. How It Works
The FedAvg algorithm, communication rounds, aggregation strategies, and convergence.
3. Frameworks
PySyft, TensorFlow Federated, Flower, and NVIDIA FLARE for FL development.
4. Privacy
Differential privacy, secure aggregation, homomorphic encryption, and threat models.
5. Applications
Healthcare, finance, mobile keyboards, autonomous vehicles, and cross-organization ML.
6. Best Practices
Non-IID data handling, communication efficiency, security considerations, and deployment.
What You'll Learn
By the end of this course, you will be able to:
Understand FL Foundations
Grasp why federated learning exists, how it preserves privacy, and where it fits in the ML landscape.
Build FL Systems
Implement federated training using Flower, TensorFlow Federated, and PySyft frameworks.
Apply Privacy Techniques
Add differential privacy, secure aggregation, and encryption to federated systems.
Design FL Applications
Architect federated solutions for healthcare, finance, and mobile applications.