In my experience federated learning gets talked about a lot more than actually used.

I'm researching federated learning. It's currently used in a number of contexts including the Google and Apple keyboards on your Android and iOS devices respectively.

Federated learning is a very active field of research. There are no simple frameworks that folks can easily operationalize. Most do not have problems that necessitate federated learning—although the growth in data privacy laws, public-private partnerships, and need to build models on privately held data (think commercial partnerships) are making it more and more prevalent.

That's very interesting. What is the focus of your research in FL?

I am studying aspects of compression (i.e., gradient compression) in federated learning. I also study problems and applications of federated learning to public-private partnerships (i.e., the cross-silo setting as opposed to the cross-domain setting).

I am not sure this is true. Do you have any numbers/data backing this?