I too would be interested in understanding this better.
Let's say we're building a medical segmentation model, which takes a patient image and outlines a tumour (or some other feature that's unique to them). I am not sure this matters here, but let's say the model is a basic 2D U-net. Image pixels in, binary pixel labels out (cancer/non-cancer).
At a high level, how would a differentially-private setup work for training such a model across multiple institutions without pooling their patient data?
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Can you say more about what differential privacy is and how it works, for those of us who don't know or don't remember?
I too would be interested in understanding this better.
Let's say we're building a medical segmentation model, which takes a patient image and outlines a tumour (or some other feature that's unique to them). I am not sure this matters here, but let's say the model is a basic 2D U-net. Image pixels in, binary pixel labels out (cancer/non-cancer).
At a high level, how would a differentially-private setup work for training such a model across multiple institutions without pooling their patient data?
https://pair.withgoogle.com/explorables/private-and-fair/