#Federated Learning

Articles about Federated Learning — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with federated learning. ← All posts

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Pratik Dhanave · ·6 min read

Federated Learning — Training Without Centralizing the Data

The most direct way to protect data is to never move it. Federated learning trains a shared model across many devices or organizations without their data ever leaving home — the model travels to the data, learns locally, and only the lessons (not the data) come back. It's how your phone's keyboard improves without uploading what you type, and how hospitals can build a shared model without sharing patient records. This post is about learning from data you're never allowed to see.

The most direct way to protect data is to never move it. Federated learning trains where the data lives (phones, hospitals) and shares only model updates — data minimization by architecture. But updates leak too, so it's combined with differential privacy and secure aggregation; it changes the threat model, not the threat.

#Privacy#Federated Learning#Machine Learning#Distributed Systems#Security

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.