Federated Learning
Federated Learning trains AI models across devices without sharing raw data. Each device learns locally and shares only model updates with a central server. It's the privacy-first approach powering modern AI in healthcare, mobile, and finance.
Federated Learning (FL) is a machine learning technique that enables AI models to be trained across many devices or servers without centralizing private data. Instead of sending your data to the cloud, your device trains the model locally and shares only the weight updates. A central server then aggregates these updates to improve the global model — round by round. This approach is already used in Google's Gboard keyboard, hospital diagnostic systems, and banking fraud detection. It solves one of the biggest problems in modern AI: how to train powerful models when data is sensitive, regulated, or simply too large to move. FL works with two main setups — cross-device (millions of phones) and cross-silo (a handful of institutions like hospitals). The core algorithm is FedAvg, but advanced variants like FedProx and SCAFFOLD handle real-world data heterogeneity. Privacy can be strengthened further by layering differential privacy and secure aggregation on top. Whether you're a researcher, developer, or ML practitioner, understanding federated learning is increasingly essential as AI moves to the edge.
What You'll Learn
Federated Learning Introduction
FL System Architecture
FL System Architecture: The Complete Client–Server Topology Guide
A deep-dive into how federated learning systems are physically wired together. Covers the canonical…
56 minFederated Learning: Training Rounds Explained
A visual, story-driven guide to how federated learning works through repeated training rounds. Usin…
24 minFederated Learning: Local Training on Device — A Complete Visual Guide
Learn how federated learning trains one shared model across phones, hospitals, and IoT devices with…
22 minModel Aggregation in Federated Learning: FedAvg Guide
A clear, example-driven guide to how a federated learning server merges client model updates into o…
16 minFederated Learning: The Communication Flow, Explained
A visual, beginner-friendly guide to federated learning — how a server and many clients train one s…
27 minFedAvg Algorithm
FedAvg Algorithm: How Federated Averaging Works Step by Step
A clear, visual walkthrough of the FedAvg (Federated Averaging) algorithm. Using a cooking-competit…
20 minClient Selection & Sampling in Federated Learning
A visual guide to how federated learning picks which clients train each round. Using a pollster ana…
16 minLocal SGD and Weight Updates in Federated Learning
A visual, hands-on guide to the client-side inner loop of federated learning. Using a foggy-mountai…
17 minWeighted Averaging of Model Updates in Federated Learning
A visual guide to how a federated server combines client model updates. Using a juice-factory analo…
17 minHands-On: FedAvg from Scratch in Python
A build-it-yourself walkthrough that implements Federated Averaging from scratch in pure NumPy — no…
17 min