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Research Blog: Federated Learning: Collaborative Machine Learning without Centralized Training Data
Great stuff from Google - this is really nifty stuff for large-scale privacy-preserving machine learning usage:

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.

Federated Learning allows for smarter models, lower latency, and less power consumption, all while ensuring privacy. And this approach has another immediate benefit: in addition to providing an update to the shared model, the improved model on your phone can also be used immediately, powering experiences personalized by the way you use your phone.

Papers: ,
google  ml  machine-learning  training  federated-learning  gboard  models  privacy  data-privacy  data-protection 
21 days ago by jm

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