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If I am understanding this correctly, this is pretty damn cool. I got 15 minutes of research on it, but no better way to get corrected than be wrong on the internet.

Essentially it seems that they can statistical magic "fuzz" the training set in such a way that it becomes very difficult for the model to leak information from the training set, while still providing the same output whether or not that exact info was in the training set. So I suppose the goal would be something like the ability to train on medical data, while making it so the model won't be able to complete the prompt "Workaccount 2 has a serious medical condition called ______" and would give the same response regardless of whether or not I was present in the database.

The actual weights: https://huggingface.co/google/vaultgemma-1b

> VaultGemma is a variant of the Gemma family of lightweight, state-of-the-art open models from Google. It is pre-trained from the ground up using Differential Privacy (DP). This provides strong, mathematically-backed privacy guarantees for its training data, limiting the extent to which the model's outputs can reveal information about any single training example.

> VaultGemma was trained using Tensor Processing Unit (TPU) hardware TPUv6e. Training large language models with the significant computational overhead of differential privacy requires specialized hardware. TPUs are designed to handle the massive computations involved, offering the performance, memory, and scalability necessary to train models like VaultGemma efficiently and sustainably.

Seems like it requires TPUs to run, as DP has a huge performance impact, so we're unlikely to see this in homelabs and similar environments, as far as I understand.

Edit: On second read, the TPUs were only used for training, but no description if anything specific for the hardware is needed, so assuming it's fine with a regular GPU?

Ignoring what this model architecture could do and just considering what this model does do, why would I (or anyone) want to run this model (locally) to do <insert use-case>? Is it entirely a proof-of-concept for future training on medical data? Are they looking to use this to attempt to ethically justify training on (free-tier) user's personal data via the application of noise to the training data?

This could be very good for scaling data while avoiding copyright claims since the copyright argument is a lot weaker (at least to the layman) if no memorization is happening. It even may open the door to Snow Crash like distributed training where people feed the model continuous streams of data of their computer use or even daily lives without worrying about PII leakage

Can someone explain what this actually means? I assume this still runs on Google's cloud so it's not 'private' in any meaningful sense.

Yes

Will it leak data if you fine tune with DP logic ?