Surely many are somewhat suspicious with Google services and how your software collects data to improve the user experience. Its algorithms of artificial intelligence and automatic learning until now had to communicate with its gigantic cloud, something that posed certain threats to our privacy, but that already is changing.
The company has begun to take advantage of the so-called “federated learning”, a type of procedure that takes advantage of the ability of our smartphones to train the system and improve it without Google knowing virtually nothing. The benefits of artificial intelligence are enjoyed and they also do so while protecting our usage data from our devices.
Federated learning on your mobile, not the cloud
Artificial intelligence is nourished by our data and indirect “training” to which we submit to these algorithms by the mere fact of using our devices and access the services that collect that data. Normally when we do this data is sent to Google servers and other vendors to be analyzed in order to improve the user experience.
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However Google uses that concept called Federated Learning in which “our device downloads an updated model, improving it by learning the data you are using on your phone and then summarizes those changes in a specific update.” This update is sent to the cloud, but encrypted and later combined with that of many other users. No data are stored as such, but those encrypted summaries that allow to go then updating those models of use.
The example of this system we have in Gboard, the virtual keyboard of Google that is showing suggestions as we type. Depending on whether or not you are successful and that activity is improving the model of recommendations and suggestions of terms as we write, but much of that “training” occurs in our mobile phone.
This method also has very little impact on the battery – in fact is used when we are loading the mobile – and the connections are also made via WiFi. There is no danger that the data will be sent to Google as it is, since the system uses algorithms that only decrypt those updates received from users if a minimum number of users participate in that training.