When big tech firms use Artificial Intelligence (AI) machine learning to improve their software, the process is usually a very centralised one.Companies like Google and Apple first gather information about how you use their apps, collect it in one place, and then use that aggregated data to train their new AI algorithms. And the end result for users could be anything from sharper photos, like the ones produced by Google RAISR, on your phone’s camera, to better a search function in your amazing E-Mail app. Up until now this method of training the world’s AI’s has been good enough for many companies, but the back and forth of updating apps and gathering feedback is, to say the least, time consuming. And it’s not great for user privacy either as companies have to store data on how you use your apps on their servers which as we’ve seen time and time again is like putting the chickens in with the fox. So, now to try and address these problems, Google is experimenting with a brand new method of AI training it calls Federated Learning, or Federated AI. And they say it will revolutionise how we train AI’s at scale, data privacy, and a whole host of other things. As the name implies, the Federated Learning approach is all about decentralising the work of AI. Instead of collecting user data in one place on Google’s servers and training algorithms with it the teaching process happens directly on each end user’s device. Essentially, your phone’s CPU is now being recruited to help train Google’s AI. At the moment Google is currently testing its Federated Learning method using its keyboard app, Gboard, on Android devices. When Gboard shows users suggested Google searches based on their messages, the app will remember what suggestions they took notice of and which they ignored. This information is then used to personalise the app’s algorithms directly on users’ phones. In order to carry out this training, Google incorporated a slimmed-down version of its machine learning software, TensorFlow, into the Gboard app itself. The changes are then sent back to Google, which aggregates the, and issues an update to the app for all its users.WHY THIS MATTERS IN BRIEF
One of the biggest problems with training AI’s is the shortage of data, and the issue of privacy, Federated AI Learning overcomes this problem by training AI’s on the devices and smartphones at the network’s edge.
