Google presents Federated Learning

Standard machine learning approaches requires centralizing the training data on one machine or in a datacenter. Google has built a robust cloud infrastructures for processing this data. Now for models trained from user interaction with mobile devices, they are introducing an additional approach: Federated Learning.

 

Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from storing data in the cloud. This goes beyond the use of local models that make predictions on mobile devices (like the Mobile Vision API and On-Device Smart Reply) by bringing model training to the devices as well.

 

It works like this: your device downloads the current model, improves it by learning from data on your phone, and then summarizes the changes as a small focused update. Only this update to the model is sent to the cloud, using encrypted communication, where it is immediately averaged with other user updates to improve the shared model. All the training data remains on your device, and no individual updates are stored in the cloud.

The system then needs to communicate and aggregate the model updates in a secure, efficient, scalable, and fault-tolerant way. It’s only the combination of research with this infrastructure that makes the benefits of Federated Learning possible.

 

Source: Google