Google's New Approach for Machine Learning Focuses on User Privacy, Efficiency

Google researchers have come up with a method for machine learning that tackles two of the technology's biggest pain points to date: End-user privacy and device and network resource consumption. It's called Federated Learning, and while still in the labs, could have a profound influence going forward. Here are the details from Google's official blog post: 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 the need to store the data in the cloud. 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.