10 ms·

Not knowing enough about this, my first impression is: How does Federated Learning cope with ... FAKE NEWS! (for example).

• brk 9y ago
  It is mostly applicable for taking data sourced at an endpoint (e.g. Mobile phone) and running what is essentially a refinement to the learning. A key component is that to analyze the data in the cloud for the same refinement would mean sending the data to the cloud, which the user may not want, and may also be bandwidth intensive. For fake news, the data is already in the cloud, being pushed down to the users device. A user could mark something as 'fake' (via explicit action, or possibly inaction), and that 'mark' is uploaded and the data is analyzed by central compute for refinement. To be clear, that is NOT what this paper is about, I am saying fake news would be a bad use, because the data is not being sourced by the user, only viewed and possibly marked. A better example than the gboard gesture learning refinements might be in the form of an app that acts as a dashcam (leaving aside the kludgeyness of that). The user could mark things like proper recognition of street signs, traffic lights, or brands/models of vehicles. The phone would then analyze those classification, compute a diff to the algorithm, send just that diff to the cloud (summarizing a lot). Multiply that by 1,000,000 users, and now you have a refined data set that did not require sending 100,000,000,000 images to the cloud for analysis.