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I don't work with ML for my day job but find it exhilaratingly interesting. (true story!) When I first read this I was thinking: surely we can already do distr

I don't work with ML for my day job but find it exhilaratingly interesting. (true story!) When I first read this I was thinking: surely we can already do distributed learning, isnt that what for example SparkML does? Is the benefit of this in the outsourcing of training of a large model to a bunch of weak devices?

nudpiedo 10y ago
Just that a few teams at google need more CPU power and cannot get more budge than their peer teams... perhaps even the publication of a paper like that is rewarded internally in the company for the publishers. In spite of encrypted communication between both parts, I am not sure how they will trust clients, especially since recently has been a public call for generate (and now train) faked data in user's client. Perhaps a statistically random tests to ensure that client's code has not been tampered. In the other hand no one speaks about energy/battery consumption of the clients (you got 8 cores and a gpu in your pocket right? Finally there is an application a part of videogames which will take profit of them).