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