Federated Learning of a Mixture of Global and Local Models

Mixing a Shared Model with Personal Ones Cuts Back-and-Forth

Imagine your phone helps train a smart model but never sends your photos away.
This idea mixes one shared model with tiny local models that live on each device, so devices learns from their own data and still join a bigger team.
The trick is finding the right balance, and it show that doing some work locally can mean less communication with the cloud.
We used new update rules that ask devices to talk less, which makes training faster and uses less bandwidth, and it still keeps models accurate when data on phones are not the same.
Personalizing the model for each person also helps, it reduce the need to exchange many updates.
The result: stronger privacy, faster updates and happy batteries because phones dont need to chat so much.
This approach could make smart features work better on your device, while keeping your data more private and using way less network.
Try to imagine AI that learns with you, not from you.

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Federated Learning of a Mixture of Global and Local Models

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