On one hand, updating ML models without seeing the data sounds like madness. It puts seemingly impossible constraints on QA: Meaningful model improvements typically come through careful error analysis, discovering entire classes of errors that call for different approaches, bug fixes, new training data types, preprocessing and model architectures. Not uptraining with random amounts of unknown data.

On the other hand, federated learning is something that's clearly desirable, a compelling pitch. So you could argue these "technical impossibilities" might be overcome with enough cleverness, eyeballs and complex enough ecosystems.

I don't think we're nowhere near, but it's one of those paradigm shifts that have my ears perked up. I'm curious what'll come out of this, cheering on Andrew Trask and his OpenMined from the side lines!