[giorgosera]

Hey! Hope someone can help me with this question. How does the data exploration phase look like in a federated learning context? Most of the times (most probably always) before applying any ML algorithm we'd look at the data and explore it. How can this be done in this case if data is not available to see? Even in the example in the blog post of Flower the dataset is loaded directly without any pre-processing (which is usually the case in real life).

[oofbey]

You can’t explore the data. Or rather you have to do it on a different dataset that you collect the traditional way. Federated learning is hard.

[giorgosera]

Thanks! Based on your own experience is that how you handled it? How did it work out in your case?

[oofbey]

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

[techwizrd]

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.

[giorgosera]

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

[techwizrd]

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).

[giorgosera]

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