[nl]

So I'm most familiar with using this in cases like machine learning on private data in cloud environments where you want to make it impossible for the cloud operator to see the data you are using.

I think there are usecases like this outside the mobile _phone_ that are interesting. For example on-device learning for edge devices where the device is not under your control.

[saagarjha]

See the thing here is that if the device is not under "your" control ("you" being a company or something, and the device being owned by a user) I don't think they will really appreciate you using their hardware to train your model in a way they don't get to see. Why would I want to support this on my own phone?

[nl]

> I don't think they will really appreciate you using their hardware to train your model in a way they don't get to see.

This absolutely isn't the case. I know a number of vendors who are deploying edge ML capacity in satellites where the use case is for "agencies" to deploy ML algorithms that they (the vendors) cannot see.