Researchers at Ohio State University have developed a method for building a machine learning algorithm from data gathered from a variety of connected devices. There are two cool things about their model worth noting. The first is that the model is distributed and second, it can keep data private.
The researchers call their model Crowd-ML and the idea is pretty basic. Each device runs a version of a necessary app, much like one might run a version of SETI@home or other distributed computing application, and grabs samples of data to send to a central server. The server can tell when enough of the right data has been gathered to “teach” the computer and only grabs the data it needs, ensuring a relative amount of privacy.
The model uses a variant of stochastic (sub)gradient descent instead of batch processing, to grab data for machine learning, which is what makes the Crowd-ML…
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