[ulnarkressty]

How would this actually work in practice? Do I ask the user to utter specific words then train on that? How is it different from the traditional speech recognition that I need to 'train' to work better on my voice?

The Holy Grail would be to train the model while using it, without any friction. I don't think these methods support that though.

[danieljanes]

One of the Flower maintainers here. The code example is primarily meant as a demonstrator to show that it's possible to fine-tune these models in a federated way on devices as small as a Raspberry Pi 5.

The bigger takeaway is that we're close to being able to train/fine-tune models with much better performance by accessing vastly more data on the edge, in a federated way.

[lfmunoz4]

The device on the edge creates the data but must also label it, right?

[jafermarq]

If the task requires labels yes. Alternatively, an auxiliary model (not part of the training) can generate pseudo labels and use those during training. A more general approach (for which a few works in FL have proven to work pretty well) would do instead un/semi-supervised training.