Free Lesson
Federated Learning Without Sharing Data
30 min
Nov 18, 2026 3:00 AM
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What you'll learn
Understand how federated learning works
Learn how a shared model is trained across many data owners without moving data, and how FedAvg combines the updates.
Train a shared model across clients with Flower
Set up three clients to train one model with FedAvg in Flower, and explain how the client and server code fit together.
Decide when federated learning fits a project
Assess whether a use case suits federated learning based on data distribution, privacy needs, and site infrastructure.
Why this topic matters
The best data for AI is often the data that cannot be collected. Privacy laws, regulations and trust keep it locked inside hospitals, banks and devices. Federated learning breaks that deadlock: the model goes to the data, learns there, and only the updates come back. It’s already used in phones, healthcare and finance, and is becoming a key skill for anyone working with sensitive data. Intuition and a working example are the best way to start.
