Federated Learning with Non-IID Data

How phones teach AI without sharing your photos

Your phone and IoT gadget can train a single smart model without sending your pictures or messages to the cloud.
This keeps privacy and let edge devices learn using their own data.
But there's a catch: when each device sees very different kinds of data the shared model can get much worse.
Tests show accuracy can fall by up to 55% when a device only sees one class.
The root cause is that models trained on different piles of data start to drift apart, making the final model confused.
A simple fix helps: give every device a tiny common sample of data to learn from.
With only about 5% of shared data globally the results improve a lot — accuracy rises roughly 30% in experiments.
This approach keeps most data local, protects privacy, and make smart assistants more reliable.
It's a small trade that can make machine learning on phones much stronger, and it's simple enough to use on many devices now.

Read article comprehensive review in Paperium.net:
Federated Learning with Non-IID Data

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