Laugh out loud. This was the premise of what we were doing 24/7 last year. I really really doubt we were the only ones doing this. Anytime you have highly valuable data that you can't share specifically but want to share the aggregated ML results of, this is how you do it.
I see. My understanding from the article was that it was a novel approach to do the ML on the device but then efficiently transmit and somehow combine the results together into a larger model.
I'm not an expert on ML (clearly). What format does the model actually take when you share it? Is it raw data (like weights for neurons or something) + the configuration of the algorithms?
I understand deanonymizing data and sharing aggregated results, but I thought actually sharing that data via essentially encoding into an algorithm and then sharing that algorithm is quite different.
Pretty much. It's batch learning; they train small batches on the phones and upload the differences. It looks like they have a fancy way to keep the data compressed, train during ideal times and encrypting the data until it gets averaged into the model.
I'm not sure this is ready for decentralized P2P yet, but I would love to see someone working towards that.
